artificial intelligence in disease diagnosis a systematic literature review

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Yogesh kumar.

1 Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India

Apeksha Koul

2 Shri Mata Vaishno Devi University, Jammu, India

Ruchi Singla

3 Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India

Muhammad Fazal Ijaz

4 Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Introduction

Healthcare is shaping up in front of our eyes with advances in digital healthcare technologies such as artificial intelligence (AI), 3D printing, robotics, nanotechnology, etc. Digitized healthcare presents numerous opportunities for reducing human errors, improving clinical outcomes, tracking data over time, etc. AI methods from machine learning to deep learning assume a crucial function in numerous well-being-related domains, including improving new clinical systems, patient information and records, and treating various illnesses (Usyal et al. 2020 ; Zebene et al. 2019 ). The AI techniques are also most efficient in identifying the diagnosis of different types of diseases. The presence of computerized reasoning (AI) as a method for improved medical services offers unprecedented occasions to recuperate patient and clinical group results, decrease costs, etc. The models used are not limited to computerization, such as providing patients, “family” (Musleh et al. 2019 ; Dabowsa et al. 2017 ), and medical service experts for data creation and suggestions as well as disclosure of data for shared evaluation building. AI can also help to recognize the precise demographics or environmental areas where the frequency of illness or high-risk behaviors exists. Researchers have effectively used deep learning classifications in diagnostic approaches to computing links between the built environment and obesity frequency (Bhatt et al. 2019 ; Plawiak et al. 2018 ).

AI algorithms must be trained on population-representative information to accomplish presentation levels essential for adaptable “accomplishment”. Trends, such as the charge for putting away and directing realities, information collection through electronic well-being records (Minaee et al. 2020 ; Kumar 2020 ), and exponential client state of information, have made a data-rich medical care biological system. This enlargement in health care data struggles with the lack of well-organized mechanisms for integrating and reconciling these data ahead of their current silos. However, numerous frameworks and principles facilitate summation and accomplish adequate data quantity for AI (Vasal et al. 2020 ). The challenges in the operational dynamism of AI technologies in healthcare systems are immeasurable despite the information that this is one of the most vital expansion areas in biomedical research (Kumar et al. 2020 ). The AI commune must build an integrated best practice method for execution and safeguarding by incorporating active best practices of principled inclusivity, software growth, implementation science, and individual–workstation interaction. At the same time, AI applications have an enormous ability to work on patient outcomes. Simultaneously, they could make significant hazards regarding inappropriate patient risk assessment, diagnostic inaccuracy, healing recommen­dations, privacy breaches, and other harms (Gouda et al. 2020 ; Khan and Member 2020 ).

Researchers have used various AI-based techniques such as machine and deep learning models to detect the diseases such as skin, liver, heart, alzhemier, etc. that need to be diagnosed early. Hence, in related work, the techniques like Boltzmann machine, K nearest neighbour (kNN), support vector machine (SVM), decision tree, logistic regression, fuzzy logic, and artificial neural network to diagnose the diseases are presented along with their accuracies. For example, a research study by Dabowsa et al. ( 2017 ) used a backpropagation neural network in diagnosing skin disease to achieve the highest level of accuracy. The authors used real-world data collected from the dermatology department. Ansari et al. ( 2011 ) used a recurrent neural network (RNN) to diagnose liver disease hepatitis virus and achieved 97.59%, while a feed-forward neural network achieved 100%. Owasis et al. ( 2019 ) got 97.057 area under the curve by using residual neural network and long short-term memory to diagnose gastrointestinal disease. Khan and Member ( 2020 ) introduced a computerized arrangement framework to recover the data designs. They proposed a five-phase machine learning pipeline that further arranged each stage in various sub levels. They built a classifier framework alongside information change and highlighted choice procedures inserted inside a test and information investigation plan. Skaane et al. ( 2013 ) enquired the property of digital breast tomosynthesis on period and detected cancer in residents based screening. They did a self-determining dual analysis examination by engaging ladies of 50–69 years and comparing full-field digitized mammography plus data building tool with full-field digital mammography. Accumulation of the data building tool resulted in a non-significant enhancement in sensitivity by 76.2% and a significant increase by 96.4%. Tigga et al. ( 2020 ) aimed to assess the diabetic risk among the patients based on their lifestyle, daily routines, health problems, etc. They experimented on 952 collected via an offline and online questionnaire. The same was applied to the Pima Indian Diabetes database. The random forest classifier stood out to be the best algorithm. Alfian et al. ( 2018 ) presented a personalized healthcare monitoring system using Bluetooth-based sensors and real-time data processing. It gathers the user’s vital signs data such as blood pressure, heart rate, weight, and blood glucose from sensor nodes to a smartphone. Katherine et al. ( 2019 ) gave an overview of the types of data encountered during the setting of chronic disease. Using various machine learning algorithms, they explained the extreme value theory to better quantify severity and risk in chronic disease. Gonsalves et al. ( 2019 ) aimed to predict coronary heart disease using historical medical data via machine learning technology. The presented work supported three supervised learning techniques named Naïve Bayes, Support vector machine, and Decision tree to find the correlations in coronary heart disease, which would help improve the prediction rate. The authors worked on the South African Heart Disease dataset of 462 instances and machine learning techniques using 10-fold cross-validation. Momin et al. ( 2019 ) proposed a secure internet of things-based healthcare system utilizing a body sensor network called body sensor network care to accomplish the requirements efficiently. The sensors used analogue to digital converter, Microcontroller, cloud database, network, etc. A study by Ijaz et al. ( 2018 ) has used IoT for a healthcare monitoring system for diabetes and hypertension patients at home and used personal healthcare devices that perceive and estimate a persons’ biomedical signals. The system can notify health personnel in real-time when patients experience emergencies. Shabut et al. ( 2018 ) introduced an examination to improve a smart, versatile, empowered master to play out a programmed discovery of tuberculosis. They applied administered AI method to achieve parallel grouping from eighteenth lower request shading minutes. Their test indicated a precision of 98.4%, particularly for the tuberculosis antigen explicit counteracting agent identification on the portable stage. Tran et al. ( 2019 ) provided the global trends and developments of artificial intelligence applications related to stroke and heart diseases to identify the research gaps and suggest future research directions. Matusoka et al. ( 2020 ) stated that the mindfulness, treatment, and control of hypertension are the most significant in overcoming stroke and cardiovascular infection. Rathod et al. ( 2018 ) proposed an automated image-based retrieval system for skin disease using machine learning classification. Srinivasu et al. ( 2021a , b ) proposed an effective model that can help doctors diagnose skin disease efficiently. The system combined neural networks with MobileNet V2 and Long Short Term Memory (LSTM) with an accuracy rate of 85%, exceeding other state-of-the-art deep models of deep learning neural networks. This system utilized the technique to analyse, process, and relegate the image data predicted based on various features. As a result, it gave more accuracy and generated faster results as compared to the traditional methods. Uehara et al. ( 2018 ) worked at the Japanese extremely chubby patients utilizing artificial brainpower with rule extraction procedure. They had 79 Non-alcoholic steatohepatitis, and 23 non- Non-alcoholic steatohepatitis patients analyse d to make the desired model. They accomplished the prescient exactness by 79.2%. Ijaz et al. ( 2020 ) propose a cervical cancer prediction model for early prediction of cervical cancer using risk factors as inputs. The authors utilize several machine learning approaches and outlier detection for different pre-processing tasks. Srinivasu et al. ( 2021a , b ) used an AW-HARIS algorithm to perform automated segmentation of CT scan images to identify abnormalities in the human liver. It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.

To fully understand how AI assists in the diagnosis and prediction of a disease, it is essential to understand the use and applicability of diverse techniques such as SVM, KNN, Naïve Bayes, Decision Tree, Ada Boost, Random Forest, K-Mean clustering, RNN, Convolutional neural networks (CNN), Deep-CNN, Generative Adversarial Networks (GAN), and Long short-term memory (LSTM) and many others for various disease detection system (Owasis et al. 2019 ; Nithya et al. 2020 ). We conducted an extensive survey based on the machine and deep learning models for disease diagnosis. The study covers the review of various diseases and their diagnostic methods using AI techniques. This contribution explains by addressing the four research questions: RQ1. What is the state-of-the-art research for AI in disease diagnosis? RQ2. What are the various types of diseases wherein AI is applied? RQ3. What are the emergent limitations and challenges that the literature advances for this research area? RQ4.What are the future avenues in healthcare that might benefit from the application of AI? The rest of the work is organized into various sections. Initially, a brief description of AI in healthcare and disease diagnosis using multiple machines and deep learning techniques is given in Sect.  1 . Then, it is named an introduction that includes Fig.  1 to describe all the papers taken from different organized sources for various diseases in the contribution sub-section. Materials and Methods is named as Sect.  2 , which includes the quality assessment and the investigation part regarding AI techniques and applications. Section  3 covers symptoms of diseases and challenges to diagnostics, a framework for AI in disease detection modelling, and various AI applications in healthcare. Section  4 includes the reported work of multiple diseases and the comparative analysis of different techniques with the used dataset, applied machine and deep learning methods with computed outcomes in terms of various parameters such as accuracy, sensitivity, specificity, the area under the curve, and F-score. In Sect.  5 , the discussion part is covered that answers the investigation part mentioned in Sect.  2 . Finally, in Sect.  6 , the work that helps researchers chooses the best approach for diagnosing the diseases is concluded along with the future scope.

Distribution of published papers for diseases diagnosis using artificial intelligence techniques

Contribution

Diseases usually are quantified by signs and symptoms. A sign is identified as an objective appearance of a disease that doctors can specify, whereas a symptom is a particular indication of the patient’s illness (Plawiak et al. 2018 ). Thus, every disease has various signs and symptoms, such as fever, which is found in countless conditions.

As shown in Fig.  1 , the number of papers reviewed under preferred reporting items for systematic reviews and Meta-Analysis (PRISMA) guidelines for different types of diseases using AI from the year 2009 to the year 2020. The present work emphasizes various diseases and their diagnostics measures using machine and deep learning classifications. To the best of our knowledge, most of the past work focused on disease diagnostics for one or two disease prediction systems. Hence, the present study explores ten different disease symptoms and their detection using AI techniques. Furthermore, this paper is unique, as it contains an elaborate discussion about various disease diagnoses and predictions based upon the extensive survey conducted for detection methods.

Materials and methods

We have directed this review according to the preferred reporting items for systematic reviews and Meta-Analysis guidelines. The survey offers the readers wide-ranging knowledge of the literature on AI (decision tree, which breaks down the dataset into smaller subsets and to build it, two types of entropy using frequencies are calculated in which X, S is a discrete random variable which occurs with probability p(i),…. p(c) and logarithm with base 2 gives the unit of bits or Shannons where entropy using the frequency table of one attribute is given as (Sabottke and Spieler 2020 )

and entropy using the frequency table of two attributes is given as

K-nearest neighbour algorithm is a supervised machine learning technique that is used to solve classification issues as well as to calculate the distance between the test data and the input to give the prediction by using Euclidean distance formula in which p, q are the two points in Euclidean n-space, and qi and pi are the Euclidean vectors starting from the origin of the space (Zaar et al. 2020 ).

Whereas regression is used to determine the relationship between independent and dependent variables. The equation Y represents it is equal to an X plus b, where Y is the dependent variable, an is the slope of the regression equation, x is the independent variable, and b is constant (Kolkur et al. 2018 )

where Y is the dependent variable, X is the independent variable; a is the intercept, b is the slope and is the residual error, Naïve Bayes which provides a way of calculating the posterior probability, P (c | x) from P(c), P(x) and P(x | c). Naïve Bayes classifier assumes that the effect of the value of an attribute (x) on a given class (c) is independent of the values of other predictors (Spann et al. 2020 )

P(c | x) is the posterior probability of class given attribute, P(x | c) is the likelihood which is the probability of the attribute given class, P(x) is the prior probability of attribute, P(c) is the prior probability of a class, k-means ( Fujita et al. 2020 ) which is used to define k centers, one for each cluster and these centres should be placed far away from each other. This algorithm also aims at minimizing an objective function which is known as squared error function, given by :

||x i -v j || is the Euclidean distance between x i -v j, Ci is the number of data points in ith cluster, C is the number of cluster center’s, convolution neural network which is a type of feed-forward artificial neural network in which the connectivity pattern between its neurons is inspired by the organization of the animal visual cortex. Convolution is the first step in the process that convolution neural network undergoes (Zhang et al. 2019 )

where (f*g)(t) = functions that are being convoluted, t = real number variable of functions f and g, g( τ ) = convolution of time function, τ ′  = first derivative of tau function, a recurrent neural network which is used for handling sequential data and its formula in which h(t) is a function f of the previously hidden state h(t − 1) and the current input x(t). The theta are the parameters of the function f is (Yang et al. 2020 )

Boltzmann machine, which optimizes the weights, a quantity related to the particular problem. Its main objective is to maximize the Consensus function (CF), which is given by the following formula (Zhou et al. 2019 )

where U i and U j are the set of units, w ij is the fixed weight, gradient descent which is an iterative process and is formulated by (Chang et al. 2018 )

where θ 1 is the next position, θ 0 is the current position, α is the small step, ∇ J θ is the direction of fastest increase) in healthcare (Zhang et al. 2017 ). The extensive survey also promotes expounding prevailing knowledge gaps and subsequent identification of paths for future research (Lin et al. 2019 ). The current study reformed the structure, which produced wide-ranging article valuation standards from earlier published articles. Articles incorporated in our research are selected using keywords like “Artificial Intelligence”, “Disease Detection”, “Disease diagnosis using machine learning”, “Disease diagnosis using deep learning”, “Artificial Intelligence in Healthcare”, and combinations of these keywords. In addition, research articles associated with the applications of AI-based techniques in predicting diseases and diagnosing them are incorporated for review. Table  1 lists the publications that are included or omitted based on a variety of criteria such as time, studies to define how old papers/articles can be accessed, the problem on which the article is based, comparative analysis of the work, methods to represent the techniques used, and research design to analyse the results that are obtained. These characteristics assisted us in carrying out the research study very quickly, without wasting time on irrelevant or unnecessary searches and investigations. The standards for inclusion and exclusion are developed by the requirements of the problem of an article.

Inclusion and exclusion parameters

Quality assessment

Research articles included in this review are identified using several quality evaluation constraints. The significance of the study is assessed based on inclusion and exclusion standards. All research articles included for review encompass machine or deep learning-based prediction models for automatically detecting and diagnosing diseases. Each research work incorporated in this study carried empirical research and had experimental outcomes. The description of these research articles is stated in a separate subsection entitled literature survey.

The comprehensive selection of research papers is carried out in four phases: (1) identifying  where records are identified through various databases. At this phase, we must do the searches we’ve planned through the abstract and citation databases we’ve chosen. Take note of how many results the searches returned. We can also include data found in other places, such as Google Scholar or the reference lists of related papers. Then, in one citation management application, aggregate all of the records retrieved from the searches. Keep in mind that each database has its own set of rules for searching for terms of interest and combining keywords for a more efficient search. As a result, our search technique may vary significantly depending on the database, (2) screening  the selection process is done transparently by reporting on decisions made at various stages of the systematic review. One of the investigators reviews the title and abstract of each record to see if the publication provides information that might be useful or relevant to the systematic review. In certain situations, the title and abstract screening is done by two investigators. They don’t split the job amongst themselves! Each investigator screens every title and abstract, and then their judgments are compared. If one of them decides to leave out an item that the other thinks should be included, they may go over the entire text together and come to a common conclusion. They can also enlist the help of a third party (usually the project manager or main investigator) to decide whether or not the study should be included. Make sure that the most acceptable justification for excluding an item is chosen. (3) Eligibility  we study the complete contents of the articles that cleared the title and abstract screening to see whether they may assist in answering our research topic. Two investigators do this full-text screening. Each one examines the entire content of each article before deciding whether or not to include it. We must note the number of articles we remove and the number of articles under each cause for exclusion in the full-text screening, just as we did in the title/abstract screening. Hence, in this stage, full-text articles are assessed and then finally are included in qualitative analysis in (4) included  phase by utilizing the Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart as depicted in Fig.  2 . In this stage, we’ll know how many papers will be included in our systematic review after removing irrelevant studies from the full-text screen. We assess how many of these studies may be included in a quantitative synthesis, commonly known as “meta-analysis,“ in the fourth and final screening stage.

PRISMA flow chart

To address the RQ1, RQ2, RQ3, and RQ4, the current survey examined the number of articles on different disease diagnoses using AI techniques from various data sources, including Psychological Information, Excerpta Medica Database, Google Scholar, PubMed, Scopus, and Web of Science. The above sources are popular sources of information for articles on AI in health informatics in previous studies. As previously explained, articles are chosen based on specified inclusion and exclusion criteria (Zhang et al. 2017 ). These were derived from (Behera et al. 2019 ), where the authors established and accepted the variations. To better understand the state of research on AI in disease detection, peer-reviewed papers are cited. The current review suggests that AI and healthcare have developed a present synergy.

Investigation

Investigation 1: Why do we need AI?

Investigation 2: What is the impact of AI on medical diagnosis and treatment?

Investigation 3: Why is AI important, and how is it used to analyse these diseases?

Investigation 4: Which AI-based algorithm is used in disease diagnosis?

Investigation 5: What are the challenges faced by the researchers while using AI models in several disease diagnoses?

Investigation 6: How are AI-based techniques helping doctors in diagnosing diseases?

Artificial intelligence in disease diagnosis

Detecting any irresistible ailment is nearly an afterward movement and forestalling its spread requires ongoing data and examination. Hence, acting rapidly with accurate data tosses a significant effect on the lives of individuals around the globe socially and financially (Minaee et al. 2020 ). The best thing about applying AI in health care is to improve from gathering and processing valuable data to programming surgeon robots. This section expounds on the various techniques and applications of artificial intelligence, disease symptoms, diagnostics issues, and a framework for disease detection modelling using learning models and AI in healthcare applications (Kumar and Singla 2021 ).

Framework for AI in disease detection modelling

AI describes the capability of a machine to study the way a human learns, e.g., through image identification and detecting pattern in a problematic situation. AI in health care alters how information gets composed, analysed, and developed for patient care (Ali et al. 2019 ).

System planning is the fundamental abstract design of the system. It includes the framework’s views, the course of action of the framework, and how the framework carries on underneath clear conditions. A solid grip of the framework design can help the client realize the limits and boundaries of the said framework. Figure  3 shows a pictorial portrayal of the ailment recognition model using utilitarian machines and profound learning classification strategies. In pre-preparing, real-world information requires upkeep and pre-preparing before being taken care of by the calculation (Jo et al. 2019 ). Because of the justifiable explanation, real-world data regularly contains mistakes regarding the utilized measures yet cannot practice such blunders. Accordingly, information pre-preparing takes this crude information, cycles it, eliminates errors, and spares it an extra examination. Information experiences a progression of steps during pre-handling (Chen et al. 2019a , b ): Information is purged by various strategies in information cleaning. These strategies involve gathering information, such as filling the information spaces that are left clear or decreasing information, such as the disposal of commas or other obscure characters. In information osmosis, the information is joined from a combination of sources. The information is then amended for any blend of mistakes, and they are quickly taken care of. Information Alteration : Data in this progression is standardized, which depends upon the given calculation. Information standardization can be executed utilizing several ways (Nasser et al. 2019 ). This progression is obligatory in most information mining calculations, as the information wants to be as perfect as possible. Information is then mutual and developed. Information Lessening : This progression in the strategy centers to diminish the information to more helpful levels. Informational collection and test information : The informational collection is segregated into parts preparing and testing informational indexes. The preparation information is utilized to gauge the actual examples of the data (Sarao et al. 2020 ). Equivalent to information needed for preparing and testing, experimental data is often replicated from a similar informational index. After the model has been pre-handled, the jiffy step is to test the accuracy of the framework. Systematic model : Analytical displaying strategies are utilized to calculate the probability of a given occurrence function given commitment factors, and it is very productive in illness expectation. It tends to imagine what the individual is experiencing in light of their info indications and prior determinations (Keenan et al. 2020 ; Rajalakshmi et al. 2018 ).

Framework for disease detection system

Medical imaging for diseases diagnosis

Clinical Imaging is seen to assign the arrangement of procedures that produce pictures of the inside part of the body. The procedure and cycles are used to take pictures of the human body for clinical purposes, such as uncovering, analysing, or looking at an injury, brokenness, and pathology (Bibault et al. 2020 ). Computed tomography (CT) scan outputs are great representations of helpful indicative imaging that encourages exact conclusion, mediation, and evaluation of harms and dysfunctions that actual advisors address consistently (Chen et al. 2017 ). Additional contemplates demonstrate overuse of Imaging, for example, X-rays or magnetic resonance imaging (MRI) for intense and complicated work, as shown in Table  2 .

Medical imaging types with their respective descriptions

Symptoms of diseases and challenges to diagnostics

The disease may be severe, persistent, cruel, or benign. Of these terms, persistent and severe have to do with the interval of a disease, lethal and begin with the potential for causing death. Additionally, different manifestations that may be irrelevant could post the warnings for more restorative severe illness or situation. The followings are a couple of diseases with their sign and indications for events:

• Heart assault signs incorporate hurt, nervousness, crushing, or feeling of breadth in the focal point of the chest that endures more than a couple of moments; agony or anxiety in different territories of the chest area; succinctness of breath; cold perspiration; heaving; or unsteadiness (Aggarwal et al. 2020 ).
• Stroke signs incorporate facial listing, arm shortcoming, the intricacy with discourse, quickly creating happiness or equalization, unexpected absence of sensation or weak point, loss of vision, puzzlement, or agonizing torment (Lukwanto et al. 2015 ).
• Reproductive wellbeing manages the signs that develop the issues such as blood misfortune or spotting between periods; tingling, copying, disturbance at genital region; agony or disquiet during intercourse; genuine or sore feminine dying; extreme pelvic/stomach torment; strange vaginal release; the sentiment of totality in the lower mid-region; and customary pee or urinary weight (Kather et al. 2019 ).
• Breast issue side effects include areola release, abnormal bosom delicacy or torment, bosom or areola skin changes, knot or thickening in or close to bosom or in the underarm zone (Memon et al. 2019 ).
• Lung issue side effects include hacking of blood, succinctness of breath, difficult breathing, consistent hack, rehashed episodes of bronchitis or pneumonia, and puffing (Ma et al. 2020 ).
• Stomach or stomach-related issue manifestations incorporate rectal dying, blood in the stool or dark stools, changes in gut properties or not having the option to control guts, stoppage, loose bowels, indigestion or heartburn, or spewing blood (Kather et al. 2019 ).
• Bladder issue manifestations include confounded or excruciating pee, incessant pee, loss of bladder control, blood in pee, waking routinely to pee around evening time to pee or wetting the bed around evening time, or spilling pee (Shkolyar et al. 2019 ).
• Skin issue indications remember changes for skin moles, repetitive flushing and redness of face and neck, jaundice, skin sores that do not disappear or re-establish to wellbeing, new development or moles on the skin, and thick, red skin with bright patches (Rodrigues et al. 2020 ).
• Emotional issues include nervousness, sadness, weariness, feeling tense, flashbacks and bad dreams, lack of engagement in daily exercises, self-destructive musings, mind flights, and fancies (Krittanawong et al. 2018 ).
• Headache issues indications (excluding ordinary strain cerebral pains) incorporate migraines that please unexpectedly, “the most noticeably awful migraine of your life”, and cerebral pain connected with extreme energy, queasiness, heaving, and powerlessness to walk (Mueller 2020 ).

Above, we have described the variety of illness signals and their symptoms. In contrast, illness recognition errors in medication are reasonably regular, can have a stringent penalty, and are only now the foundation to materialize outstandingly in patient safety. Here we have critical issues for various diagnostic types while detecting the particular diseases (Chuang 2011 ; Park et al. 2020 ).

• Analysis that is accidentally deferred wrong, or on the other hand, missed as decided from a definitive delight of more amazing data.
• Any fault or malfunction in the analytical course which is essential to a missed finding or a conceded conclusion comprises a breakdown in occasional admittance to mind; elicitation or comprehension of side effects, images, research facility result; detailing and weighing of difference investigation; and ideal development and strength arrangement or appraisal.

Healthcare applications

The healthcare system has long been an early adopter of generally innovative technologies. Today, artificial intelligence and its subset machine and deep learning are on their way to becoming a mean element in the healthcare system, from creating new health check actions to treat patient records and accounts. One of the maximum burdens physician practices today is the association and performance of organizational tasks (Fukuda et al. 2019 ). By automating them, healthcare institutions could help resolve the trouble and allow physicians to do their best, i.e., spend more time with patients. The following are the details of the artificial intelligence techniques in healthcare applications as shown in Table  3 :

Healthcare applications and their purpose

Reported work

This section highlights the best finding for different diseases with their diagnosis methods via machine and deep learning algorithms. It covers the extensive survey on various diseases such as alzheimer’s, cancer, diabetes, chronic, heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin and liver disease (Chui et al. 2020 ).

Diagnosis of Alzheimer’s disease

Alzheimer’s is a disease that worsens the dementia symptoms over several years (Zebene et al. 2019 ). During its early stage, it affects memory loss, but in the end, it loses the ability to carry the conservation and respond to the environment. Usyal et al. ( 2020 ) decided on the analysis of dementia in Alzheimer’s through investigating neuron pictures. They utilized the alzheimer’s disease neuroimaging initiative convention that comprises T1 weighted magnetic resonance information for finding. The prescient shows the precision estimated the characterization models, affectability, and explicitness esteem. Ljubic et al. ( 2020 ) presented the method to diagnose Alzheimer’s disease from electronic medical record (EMR) data. The results acquired showed the accuracy by 90% on using the SCRL dataset. Soundarya et al. ( 2020 ) proposed the methodology in which description of shrink brain tissue is used for the ancient analysis of Alzheimer’s disease. They have implemented various machine and deep learning algorithms. The deep algorithm has been considered the better solution provider to recognize the ailment at its primary stage with reasonable accuracy. Park et al. ( 2020 ) used a vast range of organizational health data to test the chance of machine learning models to expect the outlook occurrence of Alzheimer’s disease. Lin et al. ( 2019 ) proposed a method that used the spectrogram features extracted from speech data to identify Alzheimer’s disease. The system used the voice data collected via the internet of things (IoT) and transmitted to the cloud server where the original data is stored. The received data is used for training the model to identify the Alzheimer’s disease symptoms.

As seen in Fig.  4 , (Subasi 2020 ) proposed a broad framework for detecting Alzheimer’s illness using AI methods. The learning process is the process of optimizing model parameters using a training dataset or prior practice. Learning models can be predictive, predicting the future, descriptive, collecting data from input data sources, and combining them. Two critical stages are performed in machine learning and deep learning: pre-processing the vast input and improving the model. The second phase involves effectively testing the learning model and resembling the answer. Oh et al. ( 2019 ) offered a technique for demonstrating the end-to-end learning of four binary classification problems using a volumetric convolutional neural network form. The trials are performed on the ADNI database, and the results indicated that the suggested technique obtained an accuracy of 86.60% and a precision of 73.95%, respectively. Raza et al. ( 2019 ) proposed a unique AI-based examination and observation of Alzheimer’s disorder. The analysis results appeared at 82% improvement in contrast with notable existing procedures.

Alzheimer’s disease detection using artificial intelligence techniques (Subasi 2020 )

Additionally, above 95% precision is accomplished to order the exercises of everyday living, which are very reassuring regarding checking the action profile of the subject. Lodha et al. ( 2018 ) used a machine-learning algorithm to process the data obtained by neuroimaging technologies to detect Alzheimer’s in its primitive stage. It uses various algorithms like support vector machine (SVM), gradient boosting, K-nearest neighbour, Random forest, a neural network that shows the accuracy rate 97.56, 97.25, 95.00, 97.86, 98.36, respectively. Lei et al. ( 2020 ) state that to evaluate Alzheimer’s ailment, a clinical score forecast using neuroimaging data is incredibly profitable since it can adequately reveal the sickness status. The proposed structure comprises three sections: determination dependent on joint learning, highlight encoding dependent on profound polynomial arrange and amass learning for relapse through help vector relapse technique. Jo et al. ( 2019 ) performed the deep learning approach and neuroimaging data for the analytical classification of Alzheimer’s disease. Autoencoder for feature selection formed accuracy up to 98.8% and 83.7% for guessing conversion from mild cognitive impairment, a prodromal stage of Alzheimer’s disease.

A deep neural network uses neuroimaging data without pre-processing for feature collection that yields accuracies up to 96.0% for Alzheimer’s disease categorization and 84.2% for the medical council of India conversion problems (Oomman et al. 2018 ). Chen et al. ( 2017 ) hypothesized the combination of diffusivity and kurtosis in diffusion kurtosis imaging to increase the capacity of diffusion kurtosis imaging in detecting Alzheimer’s disease. The method was applied on the 53 subjects, including 27 Alzheimer’s patients, which provides an accuracy of 96.23%. Janghel et al. ( 2020 ) used a convolution neural network to improve classification accuracy. They demonstrated a deep learning technique for identifying Alzheimer’s disease using data from the Alzheimer’s disease neuroimaging initiative database, which included magnetic resonance imaging and positron emission tomography scan pictures of Alzheimer’s patients, as well as an image of a healthy individual. The experiment attained an average classification accuracy of 99.95% for the magnetic resonance imaging dataset and 73.46% for the positron emission tomography scan dataset. Balaji et al. ( 2020 ) presented the gait classification system based on machine learning to help the clinician diagnose the stage of Parkinson’s disease. They used four supervised machine learning algorithms: decision tree, support vector machine, ensemble classifier, and Bayes’ classifier, which are used for statistical and kinematic analysis that predict the severity of Parkinson’s disease.

Diagnosis of cancer disease

Artificial Intelligence methods can affect several facets of cancer therapy, including drug discovery, drug development, and the clinical validation of these drugs. Pradhan et al. ( 2020 ) evaluated several machine learning algorithms which are flexible for lung cancer recognition correlated with the internet of things. They reviewed various papers to predict different diseases using a machine learning algorithm. They also identified and depicted various research directions based on the existing methodologies. Memon et al. ( 2019 ) proposed an AI calculation-based symptomatic framework which adequately grouped the threatening and favorable individuals in the climate of the internet of things. They tried the proposed strategy on the Wisconsin Diagnostic Breast Cancer. They exhibited that the recursive element determination calculation chose the best subset of highlights and the classifier support vector machine that accomplished high order precision of 99% and affectability 98%, and Matthew’s coefficient is 99%. Das et al. ( 2019 ) proposed another framework called the watershed Gaussian-based profound learning method to depict the malignant growth injury in processed tomography pictures of the liver. They took a test of 225 pictures which are used to build up the proposed model. Yue et al. ( 2018 ) reviewed the machine learning techniques that include artificial neural networks, support vector machines, decision trees, and k-nearest neighbor for disease diagnosis. The author has investigated the breast cancer-related applications and applied them to the Wisconsin breast cancer database. Han et al. ( 2020 ) focused on the research and user-friendly design of an intelligent recommendation model for cancer patients’ rehabilitation schemes. Their prediction also achieved up to 92%. Rodrigues et al. ( 2020 ) proposed utilizing the move learning approach and profound learning approach in an IoT framework to help the specialists analyse common skin sores, average nevi, and melanoma. This investigation utilized two datasets: the first gave by the International Skin Imaging Collaboration at the worldwide Biomedical Imaging Symposium. The DenseNet201 extraction model, joined with the K nearest neighbor classifier, accomplished an exactness of 96.805% for the International Society for Bioluminescence and Chemiluminescence - International Standard Industrial Classification dataset. Huang et al. ( 2020 ) reviewed the literature on the application of artificial intelligence for cancer diagnosis and prognosis and demonstrated how these methods were advancing the field. Kather et al. ( 2019 ) used deep learning to mine clinically helpful information from histology. It can also predict the survival and molecular alternations in gastrointestinal and liver cancer. Also, these methods could be used as an inexpensive biomarker only if the pathology workflows are used. Kohlberger et al. ( 2019 ) built up a convolution neural organization to restrict and measure the seriousness of out-of-fold districts on digitized slides. On contrasting it and pathologist-reviewed center quality, ConvFocus accomplished Spearman rank coefficients of 0.81 and 0.94 on two scanners and replicated the typical designs from stack checking. Tschandl et al. ( 2019 ) build an image-based artificial intelligence for skin cancer diagnosis to address the effects of varied representations of clinical expertise and multiple clinical workflows. They also found that excellent quality artificial intelligence-based clinical decision-making support improved diagnostic accuracy over earlier artificial intelligence or physicians. It is observed that the least experienced clinicians gain the most from AI-based support. Chambi et al. ( 2019 ) worked on the volumetric Optical coherence tomography datasets acquired from resected cerebrum tissue example of 21 patients with glioma tumours of various stages. They were marked as either non-destructive or limo-invaded based on histopathology assessment of the tissue examples. Unlabelled Optical coherence tomography pictures from the other nine patients were utilized as the approval dataset to evaluate the strategy discovery execution. Chen et al. ( 2019a , b ) proposed a cost-effective technique, i.e., ARM (augmented reality microscope), that overlays artificial intelligence-based information onto the current view of the model in real-time, enabling a flawless combination of artificial intelligence into routine workflows. They even anticipated that the segmented reality microscope would remove the barrier to using AI considered to enhance the accuracy and efficiency of cancer analysis.

Diabetes detection

Diabetes Mellitus, also known as diabetes, is the leading cause of high blood sugar. AI is cost-effective to reduce the ophthalmic complications and preventable blindness associated with diabetes. This section covers the study of various researchers that worked on detecting diabetes in patients (Chaki et al. 2020 ). Kaur and Kumari ( 2018 ) used machine learning models on Pima Indian diabetes dataset to see patterns with risk factors with the help of the R data manipulation tool. They also analyse d five predictive models using the R data manipulation tool and support vector machine learning algorithm: linear kernel support vector machine, multifactor dimensionality reduction, and radial basis function.

As shown in Fig.  5 , blood glucose prediction has been categorized in three different parts: physiology-based, information-driven, and hybrid-based. Woldaregy et al. ( 2019 ) developed a compact guide in machine learning and a hybrid system that focused on predicting the blood glucose level in type 1 diabetes. They mentioned various machine learning methods crucial to regulating an artificial pancreas, decision support system, blood glucose alarm applications. They had also portrayed the knowledge about the blood glucose predictor that gave information to track and predict blood glucose levels as many factors could affect the blood glucose levels like BMI, stress, illness, medications, amount of sleep, etc. Thus blood glucose prediction provides the forecasting of an individual’s blood glucose level based on the past and current history of the patient to give an alarm to delay any complications. Chaki et al. ( 2020 ) provided detailed information to detect diabetes mellitus and self-management techniques to prove its importance to the scientists that work in this area. They also analyse d and diagnosed diabetes mellitus via its dataset, pre-processing techniques, feature extraction methods, machine learning algorithms, classification, etc. Mercaldo et al. ( 2017 ) proposed a method to classify diabetes-affected patients using a set of characteristics selected by a world health organization and obtained the precision value and recall value 0.770 and 0.775, respectively, with the help of the Hoeffding tree algorithm. Mujumdar et al. ( 2019 ) proposed the model for prediction, classification of diabetes, and external factors like glucose, body mass index, insulin, age, etc. They also analyse d that classification accuracy proved to be much more efficient with the new dataset than their used dataset. Kavakiotis et al. ( 2017 ) conducted a systematic review regarding the machine learning applications, data mining techniques, and tools used in the diabetes field to showcase the prediction and diagnosis of diabetes, its complications, and genetic conditions and situation, including the physical condition care management. After the in-depth search, it had been found that supervised learning methods characterized 85%, and the rest, 15%, were characterized by unsupervised learning methods. Aggarwal et al. ( 2020 ) demonstrated the non-linear heart rate variability in the prediction of diabetes using an artificial neural network and support vector machine. The author computed 526 datasets and obtained the classification accuracy of 90.5% with a support vector machine. Besides that, they evaluated thirteen non-linear heart rate variability parameters for the training and testing of artificial neural networks. Lukmanto et al. ( 2015 ) worked on many diabetes mellitus patients to provide an advantage for researchers to fight against it. Their main objective was to leverage fuzzy support vector machine and F-score feature selection to classify and detect diabetes mellitus. The methodology is applied to the Pima Indian Diabetes dataset, where they got an accuracy of 89.02% to predict the diabetes mellitus patients. Wang et al. ( 2017 ) proposed a weighted rank support vector machine to overcome the imbalanced problem seen during the daily dose system of drugs, leading to poor prediction results. They also employed the area under the curve (AUC) to show the model’s effectiveness and improved the average precision of their proposed algorithm. Carter et al. ( 2018 ) showcased the performance of 46 different machine learning models compared on re-sampled trained and tested data. The model obtained the area under the curve of 0.73 of training data and 0.90 of tested data. Nazir et al. ( 2019 ) proposed a technique to minutely detect the diabetic retinopathy’s different stages via tetragonal local octa pattern features that are further classified by extreme machine learning. For classifying periodic heart rate variability signals and diabetes, Swapna et al. ( 2018 ) presented a deep learning architecture. The authors used long short term memory, a convolution neural network, to extract the dynamic features of heart rate variability. They achieved an accuracy of 95.7% on using electrocardiography signals along with the support vector machine classification.

Blood glucose prediction approaches (Woldaregy et al. 2019 )

Diagnose chronic diseases

Researchers have shown that artificial intelligence helps in the streamlining care of chronic diseases. Therefore, various machine learning algorithms are developed to identify patients at higher risk of chronic disease. The other techniques based on AI are stated below (Jain et al. 2018 ).

Jain et al. ( 2018 ) presented a survey to showcase feature choice and arrangement methods to analyse and anticipate the constant illnesses. They utilized dimensionality decrease strategies to improve the presentation of AI calculation. To put it plainly, they introduced different component determination techniques and their inalienable points of interest and impediments. He et al. ( 2019 ) proposed a kernel-based structure for training the chronic illness detector to forecast and track the disease’s progression. Their approach was based on an enhanced version of a structured output support vector machine for longitudinal data processing. Tang et al. ( 2020 ) utilized deep residual networks to identify chronic obstructive pulmonary disease automatically. After gathering data from the PanCad project, which includes ex-smokers and current smokers at high risk of lung cancer, the residual network was trained to diagnose chronic obstructive pulmonary disease using computed topography scans. Additionally, they ran three rounds of cross-validation on it. With the help of three-fold cross-validation, the experiment had an area under the curve of 0.889. Ma et al. ( 2020 ) proposed the heterogeneous changed artificial neural organization to identify, divide, and determine persistent renal disappointment utilizing the web of medical things stage. The proposed strategy was named uphold vector machine and multilayer perceptron alongside the back engendering calculation. They used ultrasound images and later performed segmentation in that image. Especially in Kidney segmentation, it performed very well by achieving high results. Aldhyani et al. ( 2020 ) proposed the system that was used to increase the accuracy in detecting chronic disease by using machine learning algorithms. The machine learning methods such as Naïve Bayes, support vector machine, K nearest neighbour, and random forest were presented and compared. They also used a rough k-means algorithm to figure out the ambiguity in chronic disease to improve its performance. The Naïve Bayes method and RKM achieved an accuracy of 80.55% for diabetic disease, the support vector machine achieved 100% accuracy for kidney disease, and the support vector machine achieved 97.53% for cancer disease. Chui and Alhalabi ( 2017 ) reviewed the chronic disease diagnosis in smart health care. They provide a summarized view of optimization algorithms and machine learning algorithms. The authors also gave information regarding Alzheimer’s disease, dementia, tuberculosis, etc., followed by the challenges during the deployment phase of the disease diagnosis. Nam et al. ( 2019 ) introduced the internet of things and digital biomarkers and their relationships to artificial intelligence and other current trends. They have also discussed the role of artificial intelligence in the internet of things for chronic disease detection. Battineni et al. ( 2020 ) reviewed the applications of predictive models of machine learning to diagnose chronic disease. After going through 453 papers, they selected only 22 studies from where it was concluded that there were no standard methods that would determine the best approach in real-time clinical practice. The commonly used algorithms were support vector machine, logistic regression, etc. Wang et al. ( 2018 ) analyse d chronic kidney disease using machine learning techniques based on chronic kidney disease dataset and performed ten-fold cross-validation testing. The dataset had been pre-processed for completing and normalizing the missing data. They achieved the detection accuracy of 99% and were further tested using four patient data samples to predict the disease. Kim et al. ( 2019 ) indicated the constant sicknesses in singular patients that utilized a character repetitive neural organization to regard the information in each class as a word, mainly when an enormous bit of its information esteem is absent. They applied the Char-recurrent neural network to characterize the Korea National Health and Nutrition Examination Survey cases. They indicated the aftereffects of higher precision for the Char-recurrent neural network than for the customary multilayer perceptron model. Ani et al. ( 2017 ) proposed a patient monitoring system for stroke-affected people that reduced future recurrence by alarming the doctor and provided the data analytics and decision-making based on the patient’s real-time health parameters. That helped the doctors in systematic diagnosis followed by tailored treatment of the disease.

Heart disease diagnosis

Researchers suggest that artificial intelligence can predict the possible periods of death for heart disease patients. Thus multiple algorithms have been used to predict the heart rate severity along with its diagnosis. Escamila et al. ( 2019 ) proposed a dimensionality decrease strategy to discover the highlights of coronary illness utilizing the highlight determination procedure. The dataset used was the UCIrvine artificial intelligence vault called coronary illness which contains 74 highlights. The most remarkable precision was accomplished by the chi-square and head segment investigation alongside the irregular woods classifier. Tuli et al. ( 2019 ) proposed a Health fog framework to integrate deep learning in edge computing devices and incorporate it into the real-life application of heart detecting disease. They consisted of the hardware and software components, including body area sensor network, gateway, fogbus module, data filtering, pre-processing, resource manager, deep learning module, and ensembling module. The health fog model was an internet of things-based fog enabled model that can help effectively manage the data of heart patients and diagnose it to identify the heart rate severity.

George et al. ( 2018 ) aimed to describe the obstacles Indian nurses face in becoming active and valued members of the cardiovascular healthcare team as cardiovascular disease imposed substantial and increasing physical, psychological, societal, and financial burdens. As shown in Fig.  6 , there are numerous possible facts for health intelligent mediations to support helping cardiovascular health and decreasing hazard for cardiovascular disease. So the focus has started on the inhibition of cardiovascular disease and, more importantly, on the advancement of cardiovascular health. Several findings revealed that depression is connected with inferior cardiovascular health between adults without cardiovascular disease.

Cardiovascular health promotion and disease prevention (George et al. 2018 )

Haq et al. ( 2018 ) created a system based on machine learning to diagnose the cardiac disease guess using its dataset and worked on seven prominent feature learning-based algorithms. It was also observed that the machine learning-based decision support system assisted the doctors in diagnosing the heart patients effectively. Khan and Member ( 2020 ) proposed a framework to estimate the cardio disease using a customized deep convolution network for categorizing the fetched sensor information into the usual and unusual state. Their results demonstrated that if there would be the utmost amount of records, the multi-task cascaded convolution neural network achieved an accuracy of 98.2%. Ahmed ( 2017 ) explained the architecture for heart rate and other techniques to understand using machine learning algorithms such as K nearest neighbour classification to predict the heart attack during collecting heart rate datasets. The author also mentioned the six data types predicting heart attack in three different levels (Patel 2016 ). The dataset used consists of 303 instances and 76 attributes. They worked on a technique that could reduce the number of deaths from heart diseases. They compared various decision tree algorithms to present the heart disease diagnosis using Waikato Environment for Knowledge Analysis. They aimed to fetch the hidden patterns by using data mining techniques linked to heart disease to predict its presence. Saranya et al. ( 2019 ) proposed a cloud-based approach based on sensors for an automated disease predictive system to calculate various parameters of patients like blood pressure, heartbeat rate, and temperature. As per their knowledge, this method could reduce the time complexity of the doctor and patient in providing medical treatment quickly. The best part was that anyone could access it from anywhere. Isravel et al. ( 2020 ) presented a pre-processing approach that might enhance the accuracy in identifying the electrocardiographic signals. They evaluated the classification using different classifying algorithms such as K nearest neighbour, Naïve Bayes, and Decision tree to detect normal and irregular heartbeat sounds. Also, after trying, it was discovered that pre-processing approach increased the performance of classifying algorithms. The devices utilized for IoT set up were the LM35 sensor, Pulse sensor, AD8232 electrocardiographic sensor, and Arduino Uno. Thai et al. ( 2017 ) proposed a new lightweight method to remove the noise from electrocardiographic signals to perform minute diagnosis and prediction. Initially, they worked on the Sequential Recursive algorithm for the transformation of signals into digital format. The same was sent to the Discrete Wavelet Transform algorithm to detect the peaks in the data for removing the noises. Then features were extracted from the electrocardiographic dataset from Massachusetts Institute of Technology-Beth Israel Hospital to perform diagnosis and prediction and remove the redundant features using Fishers Linear Discriminant. Nashif et al. ( 2018 ) proposed a cloud-based heart disease prediction system for detecting heart disease using machine learning models derived from Java Based Open Access Data Mining Platform, Waikato Environment for Knowledge Analysis. They got an accuracy level of 97.53% using a support vector machine with 97.50% sensitivity and 94.94% specificity. They used an efficient software tool that trained the large dataset and compared multiple machine learning techniques. The smartphone used to detect and predict heart disease based on the information acquired from the patients. Hardware components are used to monitor the system continuously. Babu et al. ( 2019 ) aimed to determine whether the heart attack could occur using hereditary or not. Thus to work on it, initially, they collected and compared the previous data of parents with their child dataset to find the prediction and accurate values. It could help them to determine how healthy the child is. The authors used different parameters to show the dependent and independent parameters to find whether the person gets a heart attack.

Tuberculosis disease detection

AI is placed as an answer for aid in the battle against tuberculosis. Computerized reasoning applications in indicative radiology might have the option to give precise methods for recognizing the infections for low pay countries. Romero et al. ( 2020 ) performed the classification tree analysis to reveal the associations between predictors of tuberculosis in England. They worked on the American Public Health Association data ranging from demographic herd properties and tuberculosis variables using Sam Tuberculosis management. They used a machine-learning algorithm, performed data preparation, data reduction, and data analysis, and finally got the results. Horvath et al. ( 2020 ) performed the automatic scanning and analysis on 531 slides of tuberculosis, out of which 56 were from the positive specimen. They also validated a scanning and analysis system to combine fully automated microscopy using deep learning analysis. Their proposed system achieved the highest sensitivity by detecting 40 out of 56 positive slides. Sathitratanacheewin et al. ( 2020 ) developed a convolution neural network model using tuberculosis. They used a specified chest X-ray dataset taken from the national library of medical Shenzhen no. 3 hospitals and did its testing with a non-tuberculosis chest X-ray dataset taken from the national institute of health care and center. The deep convolution neural network model achieved the region of curve area under the curve by 0.9845 and 0.8502 for detecting tuberculosis and the specificity 82% and sensitivity of 72%. Bahadur et al. ( 2020 ) proposed an automatic technique to detect the abnormal chest X-ray images that contained at least one pathology such as infiltration, fibrosis, pleural effusion, etc., because of tuberculosis. This technique is based on a hierarchical structure for extracting the feature where feature sets are used in two hierarchy levels to group healthy and unhealthy people. The authors used 800 chest X-ray images taken from two public datasets named Montgomery and Shenzhen. López-Úbeda et al. ( 2020 ) explored the machine learning methods to detect tuberculosis in Spanish radiology reports. They also mentioned the deep learning classification algorithms with the purpose of its evaluation and comparison and to carry such a task. The authors have used the data of 5947 radiology reports collected from high-tech media. Ullah et al. ( 2020 ) presented the study of Raman Spectroscopy and machine learning based on principal component analysis and hierarchical component analysis to analyse tuberculosis either in positive form or negative form. They also showed Raman results which indicated the irregularities in the blood composition collected from tuberculosis-negative patients. Panicker et al. ( 2018 ) introduced the programmed technique for the location of tuberculosis bacilli from tiny smear pictures. They performed picture binarization and grouping of distinguished districts utilizing convolution neural organization. They did an assessment utilizing 22 sputum smear minuscule pictures. The results demonstrated 97.13% review, 78.4% accuracy, 86.76% F-score for predicting tuberculosis. Lai et al. ( 2020 ) compared the artificial neural network outcomes, support vector machine, and random forest while diagnosing anti-tuberculosis drugs on Taipei Medical University Wanfang Hospital patients. They selected the features via univariate risk factor analysis and literature evaluation. The authors achieved the specificity by 90.4% and sensitivity of 80%. Gao et al. ( 2019 ) investigated the applications of computed topography pulmonary images to detect tuberculosis at five levels of severity. They proposed a deep Res Net to predict the severity scores and analyse the high severity probability. They also calculate overall severity probability, separate probabilities of both high severity and low severity forces. Singh et al. ( 2020 ) worked to discover tuberculosis sores in the lungs. They proposed a computerized recognition strategy utilizing a profound learning technique known as Antialiased Convolution Neural Network proposed by Richard Zhang. Their dataset included 3D computed topography pictures, which were cut into 2D pictures. They applied division on each cutting picture utilizing UNet and Link net design.

Stroke and cerebrovascular disease detection

AI can analyse and detect stroke signs in medical images as if the system suspects a stroke in the patient. It immediately gives the signal to the patient or doctor. Researchers have proposed various methodologies to showcase the impact of AI in stroke and cerebrovascular detection (Singh et al. 2009 ). O’Connell et al. ( 2017 ) assessed the diagnostic capability and temporal stability for the detection of stroke. They observed the mostly identical patterns between the stroke patients and controls across the ten patients. They achieved the specificity and sensitivity of 90% across the research. Labovitz et al. ( 2017 ) stated the use of AI for daily monitoring of patients for the identification and medication. They achieved the improvement by 50%on plasma drug concentration levels. Abedi et al. ( 2020 ) also presented a framework to build up the decision support system using an artificial neural network, which improved patient care and outcome. Singh et al. ( 2009 ) compared the different methods to predict stroke on the cardiovascular health study dataset. They also used the decision tree algorithm for the feature selection process, principal component analysis to reduce the classification algorithm’s dimension, and a backpropagation neural network. Biswas et al. ( 2020 ) introduced an AI-based system for the location and estimation of carotid plaque as carotid intima-media thickness for the same and solid atherosclerotic carotid divider discovery and plaque estimations.

Hypertension disease detection

Researchers have found that AI has been able to diagnose hypertension by taking input data from blood pressure, demographics, etc. Krittanawong et al. ( 2018 ) summarized the review about the recent computer science and medical field advancements. They also illustrated the innovative approach of artificial intelligence to predict the early stages of hypertension. They also stated that AI plays a vital role in investigating the risk factors for hypertension. However, on the side, it has also been restricted by researchers because of its limitations in designing, etc. Arsalan et al. ( 2019 ) conducted the experiments using three publicly available datasets as digitized retinal imagery for vessel extraction (DRIVE), structured analysis of retina (STARE) for hypertension detection. They achieved the accuracy for all datasets with sensitivity, specificity, area under the curve, and accuracy of 80.22%, 98.1%, 98.2%, 96.55%, respectively. Kanegae et al. ( 2020 ) used machine learning techniques to validate the prediction of risk for new-onset hypertension. They used data in a split form for the model construction and development and validation to test its performance. The models they used were XGBoost and ensemble, in which the XGBoost model was considered the best predictor because it was systolic blood pressure nature during cardio ankle vascular. Figure  7 shows the structure of heart during its normal phase as well as in hypertension phase. When the human heart is in hypertension phase, its pulmonary arteries gets constricted because of which the right ventricle did not get the blood in to the lungs.

Pulmonary hypertension (Kanegae et al. 2020 )

Koshimizu et al. ( 2020 ) has also described artificial intelligence in pulse the executives, which was utilized to foresee the chance of circulatory strain utilizing enormous scope information. The authors also focused on the measure that was used to control blood pressure using an artificial neural network. In a nutshell, they were trying to prove that an artificial neural network is beneficial for high blood pressure organization and can also use it to create medical confirmation for the realistic organization of hypertension. Mueller et al. ( 2020 ) stated that using artificial analytic tools to the large dataset based on hypertension would generate questionable results and would also miss treatments and the potential targets. The author also stated that the vision of hypertension would be challenging to achieve and doubtlessly not happen in the future. Chaikijuraja et al. ( 2020 ) also noted the merits of using artificial intelligence to detect hypertension as artificial intelligence can recognize hypertension’s risk factors and phenotypes.

Moreover, it is used to interpret data from randomized trials that contained blood pressure targets associated with cardio vascular outcomes. Kiely et al. ( 2019 ) investigated the prescient model dependent on the medical care assets that could be sued to screen huge populaces to distinguish the patients at great danger of pneumonic blood vessel hypertension. They took the information of 709 patients from 2008 to 2016 with pneumonic blood vessel hypertension and contrasted it and separated associate of 2,812,458 who was delegated non-aspiratory blood vessel hypertension just as the prescient model was created and approved by utilizing cross approval. Kwon et al. ( 2020 ) did the past group learning of information taken on or after successive diseased people from two health care sectors to predict pulmonary hypertension using electrocardiography with the help of artificial intelligence. Sakr et al. ( 2018 ) assessed and analyse d AI strategies, such as Logit Boost, Bayesian Network Classifier, locally weighted Naïve Bayes, counterfeit neural organization, Support Vector Machine, and Random Tree Forest foresee the people to recognize hypertension. Thus, AI provides insights for hypertension healthcare and implements prescient, customized, and pre-emptive methodologies in clinical practice.

Skin disease diagnosis

Researchers have developed an AI system that can precisely group cutaneous skin problems and fill in as an auxiliary instrument to improve the demonstrative exactness of clinicians. Chakraborty et al. ( 2017 ) proposed a neural-based location technique for various skin disorders. They utilized two infected skin pictures named Basel Cell Carcinoma and Skin Angioma. Non-overwhelming arranging hereditary calculation is used to prepare the counterfeit neural organization, contrasted with the neural network particle swarm optimization classifier and neural network Caesarean Section classifier. Zaar et al. ( 2020 ) collected the clinical images of skin disease from the department of Dermatology at the Sahlgrenska University, where artificial intelligence algorithms had been used for the classification, thereby achieving the diagnosis accuracy by 56.4% for the top five suggested diseases. Kumar et al. ( 2019 ) used a dual-stage approach that combined computer vision and machine learning to evaluate and recognize skin diseases. During training and testing of the diseases, the method produced an accuracy of up to 95%. Kolkur et al. ( 2018 ) developed a system that identified skin disease based on input symptoms. They collected the data of the symptoms of ten skin diseases and got 90% above accuracy.

Liver disease detection

Researchers have found that AI can treat liver disease at its early diagnosis to work on its endurance and heal rate. Abdar et al. ( 2018 ) showed that efficient early liver disease recognition through Multilayer Perceptron Neural Network calculation depends on different choice tree calculations, such as chi-square programmed communication indicator and characterization, and relapse tree with boosting strategy. Their technique had the option to analyse and characterize the liver malady proficiently. Khaled et al. ( 2018 ) introduced an artificial neural network for the diagnosis of hepatitis virus. Protein and Histology is utilized as an info variable for the fake neural organization model, and it also showed the correct prediction of diagnosis by 93%. Spann et al. ( 2020 ) provided the strengths of machine learning tools and their potential as machine learning is applied to liver disease research, including clinical, molecular, demographic, pathological, and radiological data. Nahar and Ara ( 2018 ) explored the early guess of liver ailment using various decision tree techniques. The choice tree methods utilized were J48, Licensed Massage Therapist, Random Forest, Random Tree, REP tree, Decision Stump, and Hoeffding Trees. Their primary purpose was to calculate and compare the performances of various decision tree techniques. Farokhzad et al. ( 2016 ) used fuzzy logic for diagnosing liver sickness. Using this method, where they had two triangular membership and Gussy membership functions, they reached 79–83% accuracy.

Comparative analysis

In addition to the above mentioned reported work, the comparative analysis illustrated in Table  4 showcase the detailed information such as type of dataset, techniques, and the predicted outcomes regarding the work done by the researchers on different diseases, which in return helped the author to look for the best technique for detecting or diagnosing any particular disease.

Comparative analysis for different disease detection

From Table  4 , we can observe that AI techniques have proven to be the best for detecting diseases with improved results. AI uses machine and deep learning models that work upon training and testing data sets so that the system can see the disease and diagnose it early. In the AI-based model, we initially need to train human beings to remember the data and provide accurate results. However, it also deals with the problem. Suppose the training data produced the incorrect analysis of disease because of insufficient information, which artificial intelligence cannot factor. As a result, it will become a horrible condition for the patients as AI cannot assure us whether the prediction regarding disease detection is accurate.

On assaying the accuracy of algorithms in diagnosing the disease, deep learning classifiers have dominated over machine learning models in the field of disease diagnosis. Deep learning models have proved to be best in terms of scalp disease by 99%, Alzheimer disease by 96%, thyroid disease by 99%, 96% in skin disease, 99.37% in case of Arrhythmia disease, 95.7% in diabetic disease, while as machine learning models achieved 89% in diabetic disease, 88.67% in tuberculosis, 86.84% in Alzheimer disease, etc.

We have presented recently published research studies that employed AI-based Learning techniques for diagnosing the disease in the current review. This study highlights research on disease diagnosis prediction and predicting the post-operative life expectancy of diseased patients using AI-based learning techniques.

Investigation 1 : Why do we need AI?

We know that AI is the simulation of human processes by machines (computer systems) and that this simulation includes learning, reasoning, and self-correction. We require AI since the amount of labour we must perform is rising daily. As a result, it’s a good idea to automate regular tasks. It conserves the organization’s staff and also boosts production (Vasal et al. 2020 ).

In terms of the healthcare industry, AI in health refers to a set of diverse technologies that enable robots to detect, comprehend, act, and learn1 to execute administrative and clinical healthcare activities. AI has the potential to transform healthcare by addressing some of the industry’s most pressing issues. For example, AI can result in improved patient outcomes and increased productivity and efficiency in care delivery (Gouda et al. 2020 ). It can also enhance healthcare practitioners’ daily lives by spending more time caring for patients, therefore increasing staff morale and retention. In addition, it may potentially help bring life-saving medicines to market more quickly. Figure  8 shows the significance of AI in the medical field.

Importance of artificial intelligence in healthcare

Investigation 2 : Why is AI important, and how is it used to analyse the disease?

The emergence of new diseases remains a critical parameter in human health and society. Hence, the advances in AI allow for rapid processing and analysis of such massive and complex data. It recommends the correct decision for over ten different diseases (as mentioned in the literature) with at least 98% accuracy.

Doctors use technologies such as computed tomography scan or magnetic resonance imaging to produce a detailed 3D map of the area that needs to be diagnosed. Later, AI technology analyse s the system-generated image using machine and deep learning models to spot the diseased area’s features in seconds. As shown in the framework section, an artificial intelligence model using machine and deep learning algorithms is initially trained with the help of a particular disease dataset (Owasis et al. 2019 ). The dataset is then pre-processed using data cleaning and transformation techniques so that the disease symptoms in the form of feature vectors can be extracted and further diagnosed.

Suppose doctors do not use AI techniques. In that case, it will cause a delay in treating the patients as it is tough to interpret the scanned image manually, and it also takes a considerable amount of time. But, on the other hand, it shows that an AI technique helps the patients and helps the doctors save the patient’s life by treating them as early as possible (Luo et al. 2019 ).

Investigation 3 : What is the impact of AI in medical diagnosis?

Due to advancements in computer power, learning algorithms, and the availability of massive datasets (big data) derived from medical records and wearable health monitors. The best part of implementing AI in healthcare is that it helps to enhance various areas, including illness detection, disease classification, decision-making processes, giving optimal treatment choices, and ultimately, helping people live longer. In terms of disease diagnosis, AI has been used to enhance medical diagnosis (Chen et al. 2019a , b ). For example, the technology, which is currently in use in China, may detect hazardous tumors and nodules in patients with lung cancer, allowing physicians to provide an early diagnosis rather than sending tissue samples to a lab for testing, allowing for earlier treatment (Keenan et al. 2020 ). Figure  9 illustrates the influence of artificial intelligence and other approaches.

Comparison between AI and other techniques

Investigation 4 : Which AI-based algorithm is used in disease diagnosis?

Disease detection algorithms driven by AI demonstrated to be an effective tool for identifying undiagnosed patients with under-diagnosed, uncoded, and rare diseases. Therefore, AI models for disease detection have an ample opportunity to drive earlier diagnosis for patients in need and guide pharmaceutical companies with highly advanced, targeted diagnostics to help these patients get correctly diagnosed and treated earlier in their disease journey (Keenan et al. 2020 ). The research work mentioned in the literature has covered both machine and deep learning models for diagnosing the diseases such as cancer, diabetes, chronic, heart disease, alzheimer, stroke and cerebrovascular, hypertension, skin, and liver disease. Machine learning models, Random Forest Classifier, Logistic Regression, Fuzzy logics, Gradient Boosting Machines, Decision Tree, K nearest neighbour (KNN), and Support vector machines (SVM) are primarily used in literature. Among deep learning models, Convolutional Neural Networks (CNN) have been used most commonly for disease diagnosis. In addition, faster Recurrent Convolution Neural Network, Multilayer Perceptron, Long Short Term Memory (LSTM) have also been used extensively in the literature. Figure  10 displays the usage of AI-based prediction models in the literature.

Artificial intelligence-based prediction models

Investigation 5 : What are the challenges faced by the researchers while using AI models in several disease diagnosis?

Although AI-based techniques have marked their significance in disease diagnosis, there are still many challenges faced by the researchers that need to be addressed.

• i. Limited Data size  The most common challenge faced by most of the studies was insufficient data to train the model. A small sample size implies a smaller training set which does not authenticate the efficiency of the proposed approaches. On the other hand, good sample size can train the model better than the limited one (Rajalakshmi et al. 2018 ).
• ii. High dimensionality  Another data-related issue faced in cancer research is high dimensionality. High dimensionality is referred to a vast number of features as compared to cases. However, multiple dimensionality reduction techniques are available to deal with this issue (Bibault et al. 2020 ).
• iii. Efficient feature selection technique  Many studies have achieved exceptional prediction outcomes. However, a computationally effective feature selection method is required to eradicate the data cleaning procedures while generating high disease prediction accuracy (Koshimizu et al. 2020 ).
• iv. Model Generalizability  A shift in research towards improving the generalizability of the model is required. Most of the studies have proposed a prediction model that is validated on a single site. There is a need to validate the models on multiple sites that can help improve the model’s generalizability (Fukuda et al. 2019 ).
• v. Clinical Implementation  AI-based models have proved their dominance in medical research; still, the practical implementation of the models in the clinics is not incorporated. These models need to be validated in a clinical setting to assist the medical practitioner in affirming the diagnosis verdicts (Huang et al. 2020 ).

Investigation 6 : How artificial intelligence-based techniques are helping doctors in diagnosing diseases?

AI improves the lives of patients, physicians, and hospital managers by doing activities usually performed by people but in a fraction of the time and the expense. For example, AI assists physicians in making suggestions by evaluating vast amounts of healthcare data such as electronic health records, symptom data, and physician reports to improve health outcomes and eventually save the patient’s life (Kohlberger et al. 2019 ). Additionally, this data aids in the improvement and acceleration of decision-making while diagnosing and treating patients’ illnesses using artificial intelligence-based approaches. Not only that, AI assists physicians in detecting diseases by utilizing complicated algorithms, hundreds of biomarkers, imaging findings from millions of patients, aggregated published clinical studies, and thousands of physicians’ notes to improve the accuracy of diagnosis.

Conclusion and future scope

When it comes to disease diagnosis, accuracy is critical for planning, effective treatment and ensuring the well-being of patients. AI is a vast and diverse realm of data, algorithms, analytics, deep learning, neural networks, and insights that is constantly expanding and adapting to the needs of the healthcare industry and its patients. According to the findings of this study, AI approaches in the healthcare system, particularly for illness detection, are essential. Aiming at illuminating how machine and deep learning techniques work in various disease diagnosis areas, the current study has been divided into several sections that cover the diagnosis of alzheimer’s, cancer, diabetes, chronic diseases, heart disease, stroke and cerebrovascular disease, hypertension, skin disease, and liver disease. The introduction and contribution were covered in the first section, followed by an evaluation of the quality of the work and an examination of AI approaches and applications. Later, various illness symptoms and diagnostic difficulties, a paradigm for AI in disease detection models, and various AI applications in healthcare were discussed. The reported work on multiple diseases and the comparative analysis of different techniques with the used dataset as well as the results of an applied machine and deep learning methods in terms of multiple parameters such as accuracy, sensitivity, specificity, an area under the curve, and F-score has also been portrayed. Finally, the work that assisted researchers in determining the most effective method for detecting illnesses is finished, as in future scope. In a nutshell, medical experts better understand how AI may be used for illness diagnosis, leading to more appropriate proposals for the future development of AI based techniques.

Contrary to considerable advancements over the past several years, the area of accurate clinical diagnostics faces numerous obstacles that must be resolved and improved constantly to treat emerging illnesses and diseases effectively. Even healthcare professionals recognize the barriers that must be overcome before sickness may be detected in conjunction with artificial intelligence. Even doctors do not entirely rely on AI-based approaches at this time since they are unclear of their ability to anticipate illnesses and associated symptoms. Thus much work is required to train the AI-based systems so that there will be an increase in the accuracy to predict the methods for diagnosing diseases. Hence, in the future, AI-based research should be conducted by keeping the flaw mentioned earlier in consideration to provide a mutually beneficial relationship between AI and clinicians. In addition to this, a decentralized federated learning model should also be applied to create a single training model for disease datasets at remote places for the early diagnosis of diseases.

Acknowledgements

This research work was supported by Sejong University research fund. Yogesh Kumar and Muhammad Fazal Ijaz contributed equally to this work and are first co-authors.

Declarations

The authors declare that they have no conflict of interest.

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

This article does not contain any studies with the animals performed by any of the authors.

Informed consent was obtained from all individual participants included in the study.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Yogesh Kumar, Email: [email protected] .

Apeksha Koul, Email: moc.liamg@9oluokahskepa .

Ruchi Singla, Email: moc.oohay@algnisihcur .

Muhammad Fazal Ijaz, Email: rk.ca.gnojes@lazaf .

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Affiliations.

• 1 Department of Computer Engineering, Indus Institute of Technology and Engineering, Indus University, Ahmedabad, 382115 India.
• 2 Shri Mata Vaishno Devi University, Jammu, India.
• 3 Department of Research, Innovations, Sponsored Projects and Entrepreneurship, CGC Landran, Mohali, India.
• 4 Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006 South Korea.
• PMID: 35039756
• PMCID: PMC8754556
• DOI: 10.1007/s12652-021-03612-z

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Keywords: Alzheimer; Artificial intelligence; Cancer disease; Chronic disease; Heart disease; Tuberculosis.

© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.

Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Research output : Contribution to journal › Article › peer-review

Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

Bibliographical note

• Artificial intelligence
• Cancer disease
• Chronic disease
• Heart disease
• Tuberculosis

Access to Document

• 10.1007/s12652-021-03612-z

Fingerprint

• Diagnosis Medicine and Dentistry 100%
• Disease Medicine and Dentistry 50%
• Computer Assisted Tomography Medicine and Dentistry 25%
• Malignant Neoplasm Medicine and Dentistry 25%
• Liver Disease Medicine and Dentistry 25%
• Meta-Analysis Medicine and Dentistry 25%
• Diabetes Medicine and Dentistry 25%
• Heart Disease Medicine and Dentistry 25%

T1 - Artificial intelligence in disease diagnosis

T2 - a systematic literature review, synthesizing framework and future research agenda

AU - Kumar, Yogesh

AU - Koul, Apeksha

AU - Singla, Ruchi

AU - Ijaz, Muhammad Fazal

N1 - Publisher Copyright: © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

PY - 2023/7

Y1 - 2023/7

N2 - Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

AB - Artificial intelligence can assist providers in a variety of patient care and intelligent health systems. Artificial intelligence techniques ranging from machine learning to deep learning are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Numerous medical data sources are required to perfectly diagnose diseases using artificial intelligence techniques, such as ultrasound, magnetic resonance imaging, mammography, genomics, computed tomography scan, etc. Furthermore, artificial intelligence primarily enhanced the infirmary experience and sped up preparing patients to continue their rehabilitation at home. This article covers the comprehensive survey based on artificial intelligence techniques to diagnose numerous diseases such as Alzheimer, cancer, diabetes, chronic heart disease, tuberculosis, stroke and cerebrovascular, hypertension, skin, and liver disease. We conducted an extensive survey including the used medical imaging dataset and their feature extraction and classification process for predictions. Preferred reporting items for systematic reviews and Meta-Analysis guidelines are used to select the articles published up to October 2020 on the Web of Science, Scopus, Google Scholar, PubMed, Excerpta Medical Database, and Psychology Information for early prediction of distinct kinds of diseases using artificial intelligence-based techniques. Based on the study of different articles on disease diagnosis, the results are also compared using various quality parameters such as prediction rate, accuracy, sensitivity, specificity, the area under curve precision, recall, and F1-score.

KW - Alzheimer

KW - Artificial intelligence

KW - Cancer disease

KW - Chronic disease

KW - Heart disease

KW - Tuberculosis

UR - http://www.scopus.com/inward/record.url?scp=85122799958&partnerID=8YFLogxK

U2 - 10.1007/s12652-021-03612-z

DO - 10.1007/s12652-021-03612-z

M3 - Article

AN - SCOPUS:85122799958

SN - 1868-5137

JO - Journal of Ambient Intelligence and Humanized Computing

JF - Journal of Ambient Intelligence and Humanized Computing

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Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework and future research agenda

Journal of Ambient Intelligence and Humanized Computing

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Globally, there is a substantial unmet need to diagnose various diseases effectively. The complexity of the different disease mechanisms and underlying symptoms of the patient population presents massive challenges in developing the early diagnosis tool and effective treatment. Machine learning (ML), an area of artificial intelligence (AI), enables researchers, physicians, and patients to solve some of these issues. Based on relevant research, this review explains how machine learning (ML) is being used to help in the early identification of numerous diseases. Initially, a bibliometric analysis of the publication is carried out using data from the Scopus and Web of Science (WOS) databases. The bibliometric study of 1216 publications was undertaken to determine the most prolific authors, nations, organizations, and most cited articles. The review then summarizes the most recent trends and approaches in machine-learning-based disease diagnosis (MLBDD), considering the following factor...

Archives of Computational Methods in Engineering

Dr.Ruchi Singla

ADVANCES IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN THE DIAGNOSIS OF HEART DISEASES: AN INTEGRATIVE REVIEW (Atena Editora)

Atena Editora

Cardiovascular diseases are one of the main causes of morbidity and mortality worldwide. Accurate and early diagnosis of these conditions is essential to improve clinical outcomes and quality of life for patients. In recent years, artificial intelligence (AI) and machine learning have emerged as innovative approaches in the field of cardiology, offering promise to improve the diagnosis of heart disease. Through a search in databases such as PubMed, Scopus and Web of Science, relevant studies published in the last 8 years were identified. Inclusion criteria included original articles that investigated the use of AI and machine learning in the context of diagnosing heart disease, with an emphasis on clinical applications, validation and accuracy of the developed models. Analysis of selected studies revealed several promising applications of AI and machine learning in diagnosing heart disease. Among them, we highlight the use of deep learning algorithms in cardiovascular images for the detection of arrhythmias, the prediction of cardiovascular risk factors based on retinal fundus photographs and risk stratification in patients with heart failure. In addition, the application of convolutional neural networks has been shown to be effective in detecting arrhythmias at the level of cardiologists, achieving results comparable to human specialists. Studies have also explored the use of AI in identifying coronary artery disease through image analysis, offering a more efficient and accurate approach in diagnosing these conditions. The results of this integrative review highlight the growing interest of the scientific community in the application of AI and machine learning in cardiology, showing significant advances in this area in recent years. However, some limitations, such as the need for external validation of the developed models and the scarcity of studies in different populations, still need to be addressed for a successful clinical implementation of these technologies. In conclusion, the use of artificial intelligence and machine learning in the diagnosis of heart disease shows promise and may represent an important advance in clinical practice. Continued research and development in this area is essential to achieve more accurate and personalized diagnostic approaches, thereby improving care and outcomes for patients with cardiovascular disease.

Geetha Narasimhan

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• Published: 04 May 2024

Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy

• Clare McGenity   ORCID: orcid.org/0000-0002-0224-2340 1 , 2 ,
• Emily L. Clarke 1 , 2 ,
• Charlotte Jennings   ORCID: orcid.org/0000-0003-1584-4057 1 , 2 ,
• Gillian Matthews   ORCID: orcid.org/0000-0001-6754-0333 2 ,
• Caroline Cartlidge   ORCID: orcid.org/0000-0002-8366-4528 1 ,
• Henschel Freduah-Agyemang   ORCID: orcid.org/0009-0007-4910-5013 1 ,
• Deborah D. Stocken 1 &
• Darren Treanor   ORCID: orcid.org/0000-0002-4579-484X 1 , 2 , 3 , 4  

npj Digital Medicine volume  7 , Article number:  114 ( 2024 ) Cite this article

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• Medical imaging
• Pathogenesis

Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.

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Introduction.

Following recent prominent discoveries in deep learning techniques, wider artificial intelligence (AI) applications have emerged for many sectors, including in healthcare 1 , 2 , 3 . Pathology AI is of broad importance in areas across medicine, with implications not only in diagnostics, but in cancer research, clinical trials and AI-enabled therapeutic targeting 4 . Access to digital pathology through scanning of whole slide images (WSIs) has facilitated greater interest in AI that can be applied to these images 5 . WSIs are created by scanning glass microscope slides to produce a high resolution digital image (Fig. 1 ), which is later reviewed by a pathologist to determine the diagnosis 6 . Opportunities for pathologists have arisen from this technology, including remote and flexible working, obtaining second opinions, easier collaboration and training, and applications in research, such as AI 5 , 6 .

These are high resolution digital pathology images viewed by a pathologist on a computer to make a diagnostic assessment. Image from www.virtualpathology.leeds.ac.uk 143 .

Application of AI to an array of diagnostic tasks using WSIs has rapidly expanded in recent years 5 , 6 , 7 , 8 . Successes in AI for digital pathology can be found for many disease types, but particularly in examples applied to cancer 4 , 9 , 10 , 11 . An important early study in 2017 by Bejnordi et al. described 32 AI models developed for breast cancer detection in lymph nodes through the CAMELYON16 grand challenge. The best model achieved an area under the curve (AUC) of 0.994 (95% CI 0.983–0.999), demonstrating similar performance to the human in this controlled environment 12 . A study by Lu et al. in 2021 trained AI to predict tumour origin in cases of cancer of unknown primary (CUP) 13 . Their model achieved an AUC of 0.8 and 0.93 for top-1 and top-3 tumour accuracies respectively on an external test set. AI has also been applied to making predictions, such as determining the 5-year survival in colorectal cancer patients and the mutation status across multiple tumour types 14 , 15 .

Several reviews have examined the performance of AI in subspecialties of pathology. In 2020, Thakur et al. identified 30 studies of colorectal cancer for review with some demonstrating high diagnostic accuracy, although the overall scale of studies was small and limited in their clinical application 16 . Similarly in breast cancer, Krithiga et al. examined studies where image analysis techniques were used to detect, segment and classify disease, with reported accuracies ranging from 77 to 98% 17 . Other reviews have examined applications in liver pathology, skin pathology and kidney pathology with evidence of high diagnostic accuracy from some AI models 18 , 19 , 20 . Additionally, Rodriguez et al. performed a broader review of AI applied to WSIs and identified 26 studies for inclusion with a focus on slide level diagnosis 21 . They found substantial heterogeneity in the way performance metrics were presented and limitations in the ground truth used within studies. However, their study did not address other units of analysis and no meta-analysis was performed. Therefore, the present study is the first systematic review and meta-analysis to address the diagnostic accuracy of AI across all disease areas in digital pathology, and includes studies with multiple units of analysis.

Despite the many developments in pathology AI, examples of routine clinical use of these technologies remain rare and there are concerns around the performance, evidence quality and risk of bias for medical AI studies in general 22 , 23 , 24 . Although, in the face of an increasing pathology workforce crisis, the prospect of tools that can assist and automate tasks is appealing 25 , 26 . Challenging workflows and long waiting lists mean that substantial patient benefit could be realised if AI was successfully harnessed to assist in the pathology laboratory.

This systematic review provides an overview of performance of diagnostic tools across histopathology. The objective of this review was to determine the diagnostic test accuracy of artificial intelligence solutions applied to WSIs to diagnose disease. A further objective was to examine the risk of bias and applicability concerns within the papers. The aim of this was to provide context in terms of bias when examining the performance of different AI tools (Fig. 1 ).

Study selection

Searches identified 2976 abstracts, of which 1666 were screened after duplicates were removed. 296 full text papers were reviewed for potential inclusion. 100 studies met the full inclusion criteria for inclusion in the review, with 48 studies included in the full meta-analysis (Fig. 2 ) .

Generated using PRISMA2020 at https://estech.shinyapps.io/prisma_flowdiagram/ 144 .

Study characteristics

Study characteristics are presented by pathological subspecialty for all 100 studies identified for inclusion in Tables 1 – 7 . Studies from Europe, Asia, Africa, North America, South America and Australia/Oceania were all represented within the review, with the largest numbers of studies coming from the USA and China. Total numbers of images used across the datasets equated to over 152,000 WSIs. Further details, including funding sources for the studies can be found in Supplementary table 10 . Tables 1 and 2 show characteristics for breast pathology and cardiothoracic pathology studies respectively. Tables 3 and 4 are characteristics for dermatopathology and hepatobiliary pathology studies respectively. Tables 5 and 6 have characteristics for gastrointestinal and urological pathology studies respectively. Finally, Table 7 outlines characteristics for studies with multiple pathologies examined together and for other pathologies such as gynaepathology, haematopathology, head and neck pathology, neuropathology, paediatric pathology, bone pathology and soft tissue pathology.

Risk of bias and applicability

The risk of bias and applicability assessment using the tailored QUADAS-2 tool demonstrated that the majority of papers were either at high risk or unclear risk of bias in three out of the four domains (Fig. 3 ) . The full breakdown of individual paper scores can be found in Supplementary Table 1 . Of the 100 studies included in the systematic review, 99% demonstrated at least one area at high or unclear risk of bias or applicability concerns, with many having multiple components at risk.

a Summaries for risk of bias for all 100 papers included in the review. b Summaries for applicability concerns for all 100 papers included in the review. c , d Summaries for risk of bias for 48 papers included in the meta-analysis. d Summaries for applicability concerns for 48 papers included in the meta-analysis.

Of the 48 studies included in the meta-analysis (Fig. 3c , d ), 47 of 48 studies (98%) were at high or unclear risk of bias or applicability concerns in at least one area examined. 42 of 48 studies (88%) were either at high or unclear risk of bias for patient selection and 33 of 48 studies (69%) were at high or unclear risk of bias concerning the index test. The most common reasons for this included: cases not being selected randomly or consecutively, or the selection method being unclear; the absence of external validation of the study’s findings; and a lack of clarity on whether training and testing data were mixed. 16 of 48 studies (33%) were unclear in terms of their risk of bias for the reference standard, but no studies were considered high risk in this domain. There was often very limited detail describing the reference standard, for example the process for classifying or diagnosing disease, and so it was difficult to assess if this was an appropriate reference standard to use. For flow and timing, to ensure cases were recent enough to the study to be relevant and reasonable quality, one study was at high risk but 37 of 48 studies (77%) were at unclear risk of bias.

There were concerns of applicability for many papers included in the meta-analysis with 42 of 48 studies (88%) with either unclear or high concerns for applicability in the patient selection, 14 of 48 studies (29%) with unclear or high concern for the index test and 24 of 48 studies (50%) with unclear or high concern for the reference standard. Examples for this included; ambiguity around the selection of cases and the risk of excluding subgroups, and limited or no details given around the diagnostic criteria and pathologist involvement when describing the ground truth.

Synthesis of results

100 studies were identified for inclusion in this systematic review. Included study size varied greatly from 4 WSIs to nearly 30,000 WSIs. Data on a WSI level was frequently unavailable for numbers used in test sets, but where it was reported this ranged from 10 WSI to nearly 14,000 WSIs, with a mean of 822 WSIs and a median of 113 WSIs. The majority of studies had small datasets and just a few studies contained comparatively large datasets of thousands or tens of thousands of WSIs. Of included studies, 48 had data that could be meta-analysed. Two of the studies in the meta-analysis had available data for two different disease types 27 , 28 , meaning a total of 50 assessments included in the meta-analysis. Figure 4 shows the forest plots for sensitivity of any AI solution applied to whole slide images. Overall, there was high diagnostic accuracy across studies and disease types. Using a bivariate random effects model, the estimate of mean sensitivity across all studies was 96.3% (CI 94.1–97.7) and of mean specificity was 93.3% (CI 90.5–95.4), as shown in Fig. 5 . Additionally, the F1 score was calculated for each study ( Supplementary Materials ) from the raw confusion matrix data and this ranged from 0.43 to 1, with a mean F1 score of 0.87. Raw data and additional data for the meta-analysis can be found in Supplementary Tables 3 and 4 .

These show sensitivity ( a ) and specificity ( b ) in studies of all pathologies with 95% confidence intervals. These plots were generated by MetaDTA: Diagnostic Test Accuracy Meta-Analysis v2.01 Shiny App https://crsu.shinyapps.io/MetaDTA/ and the raw data can be found in Supplementary Table 4 92 , 93 .

95% confidence intervals are shown around the summary estimate. The predictive region shows the area of 95% confidence in which the true sensitivity and specificity of future studies lies, whilst factoring the statistical heterogeneity of studies demonstrated in this review.

The largest subgroups of studies available for inclusion in the meta-analysis were studies of gastrointestinal pathology 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , breast pathology 27 , 41 , 42 , 43 , 44 , 45 , 46 , 47 and urological pathology 27 , 48 , 49 , 50 , 51 , 52 , 53 , 54 which are shown in Table 8 , representing over 60% of models included in the meta-analysis. Notably, studies of gastrointestinal pathology had a mean sensitivity of 93% and mean specificity of 94%. Similarly, studies of uropathology had mean sensitivities and specificities of 95% and 96% respectively. Studies of breast pathology had slightly lower performance at mean sensitivity of 83% and mean specificity of 88%. Results for all other disease types are also included in the meta-analysis 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 . Forest plots for these subgroups are shown in Supplementary figure 1 . When examining cancer (48 of 50 models) versus for non-cancer diseases (2 of 50 models), performance was better for the former with mean sensitivity 92% and mean specificity 89% compared to mean sensitivity of 76% and mean specificity of 88% respectively. For studies that could not be included in the meta-analysis, an indication of best performance from other accuracy metrics provided is outlined in Supplementary Table 2 .

Of models examined in the meta-analysis, the number of sources ranged from one to fourteen and overall the mean sensitivity and specificity improved with a larger number of data sources included in the study. For example, mean sensitivity and specificity for one data source was 89% and 88% respectively, whereas for three data sources this was 93% and 92% respectively. However, the majority of studies used one or two data sources only, meaning that studies with larger numbers of data sources were comparably underrepresented. Additionally, of these models, the mean sensitivity and specificity was higher in those validated on an external test set (95% and 92% respectively compared to those without external validation (91% and 87% respectively), although it must be acknowledged that frequently raw data was only available for internal validation performance. Similar performance was reported across studies that had a slide-level and patch/tile-level unit of analysis with a mean sensitivity of 95% and 91% respectively versus a mean specificity of 88% and 90% respectively. When comparing tasks where data was provided in a multiclass confusion matrix compared to a binary confusion matrix, multiclass tasks demonstrated slightly better performance with a mean sensitivity of 95% and mean specificity of 92% compared to binary tasks with mean sensitivity 91% and mean specificity 88%. Details of these analyses can be found in Supplementary Tables 5 – 9 .

Of papers included within the meta-analysis, details of specimen preparation were frequently not specified, despite this potentially impacting the quality of histopathological assessment and subsequent AI performance. In addition, the majority of models in the meta-analysis used haematoxylin and eosin (H&E) images only, with two models using H&E combined with IHC, making comparison of these two techniques difficult. Further details of these findings can be found in Supplementary Table 11 .

AI has been extensively promoted as a useful tool that will transform medicine, with examples of innovation in clinical imaging, electronic health records (EHR), clinical decision making, genomics, wearables, drug development and robotics 75 , 76 , 77 , 78 , 79 , 80 . The potential of AI in digital pathology has been identified by many groups, with discoveries frequently emerging and attracting considerable interest 9 , 81 . Tools have not only been developed for diagnosis and prognostication, but also for predicting treatment response and genetic mutations from the H&E image alone 8 , 9 , 11 . Various models have now received regulatory approval for applications in pathology, with some examples being trialled in clinical settings 54 , 82 .

Despite the many interesting discoveries in pathology AI, translation to routine clinical use remains rare and there are many questions and challenges around the evidence quality, risk of bias and robustness of the medical AI tools in general 22 , 23 , 24 , 83 , 84 . This systematic review and meta-analysis addresses the diagnostic accuracy of AI models for detecting disease in digital pathology across all disease areas. It is a broad review of the performance of pathology AI, addresses the risk of bias in these studies, highlights the current gaps in evidence and also the deficiencies in reporting of research. Whilst the authors are not aware of a comparable systematic review and meta-analysis in pathology AI, Aggarwal et al. performed a similar review of deep learning in other (non-pathology) medical imaging types and found high diagnostic accuracy in ophthalmology imaging, respiratory imaging and breast imaging 75 . Whilst there are many exciting developments across medical imaging AI, ensuring that products are accurate and underpinned by robust evidence is essential for their future clinical utility and patient safety.

This study sought to determine the diagnostic test accuracy of artificial intelligence solutions applied to whole slide images to diagnose disease. Overall, the meta-analysis showed that AI has a high sensitivity and specificity for diagnostic tasks across a variety of disease types in whole slide images (Figs. 4 and 5 ). The F1 score ( Supplementary Materials ) was variable across the individual models included in the meta-analysis. However, on average there was good performance demonstrated by the mean F1 score. The performance of the models described in studies that were not included in the meta-analysis were also promising (see Supplementary Materials ).

Subgroups of gastrointestinal pathology, breast pathology and urological pathology studies were examined in more detail, as these were the largest subsets of studies identified (see Table 8 and Supplementary Materials ). The gastrointestinal subgroup demonstrated high mean sensitivity and specificity and included AI models for colorectal cancer 28 , 29 , 30 , 32 , 34 , 40 , gastric cancer 28 , 31 , 33 , 37 , 38 , 39 , 85 and gastritis 35 . The breast subgroup included only AI models for breast cancer applications, with Hameed et al. and Wang et al. demonstrating particularly high sensitivity (98%, 91% respectively) and specificity (93%, 96% respectively) 42 , 45 . However, there was lower diagnostic accuracy in the breast group compared to some other specialties. This could be due to several factors, including challenges with tasks in breast cancer itself, an over-estimation of performance and bias in other areas and the differences in datasets and selection of data between subspecialty areas. Overall results were most favourable for the subgroup of urological studies with both high mean sensitivity and specificity (Table 8 ). This subgroup included models for renal cancer 48 , 52 and prostate cancer 27 , 49 , 50 , 51 , 53 , 54 . Whilst high diagnostic accuracy was seen in other subspecialties (Table 8 ), for example mean sensitivity and specificity in neuropathology (100%, 95% respectively) and soft tissue and bone pathology (98%, 94% respectively), there were very few studies in these subgroups and so the larger subgroups are likely more representative.

Of studies of other disease types included in the meta-analysis (Fig. 4 ), AI models in liver cancer 74 , lymphoma 73 , melanoma 72 , pancreatic cancer 71 , brain cancer 67 lung cancer 57 and rhabdomyosarcoma 56 all demonstrated a high sensitivity and specificity. This emphasises the breadth of potential diagnostic tools for clinical applications with a high diagnostic accuracy in digital pathology. The majority of studies did not report details of the fixation and preparation of specimens used in the dataset. Where frozen section is used instead of formalin fixed paraffin embedded (FFPE) samples, this could impact the digital image quality and impact AI performance. It would be helpful for authors to consider including this information in the methods section of future studies. Only two models included in the meta-analysis used IHC and this was in combination with H&E stained samples. It would be interesting to explore the comparison between tasks using H&E when compared to IHC in more detail in future work.

Sensitivity and specificity were higher in studies with a greater number of included data sources, however few studies chose to include more than two sources of data. To develop AI models that can be applied in different institutions and populations, a diverse dataset is an important consideration for those conducting research into models intended for clinical use. A higher mean sensitivity and specificity for those models that included an external validation was identified, although many studies did not include this, or included most data for internal validation performance. Improved overall reporting of these values would allow a greater understanding of the performance of models at external validation. Performance was similar in the models included in the meta-analysis when a slide-level or patch/tile-level analysis was performed, although slide-level performance could be more useful when interpreting the clinical implications of a proposed model. A pathologist will review a case for diagnosis at slide level, rather than patch level, and so slide-level performance may be more informative when considering use in routine clinical practice. Performance was lower in non-cancer diseases when compared to cancer models, however only two of the models included in the meta-analysis were for non-cancer diseases and so this must be interpreted with caution and further work is needed in these disease areas.

Risk of bias and applicability assessments highlighted that the majority of papers contained at least one area of concern, with many studies having multiple areas of concern (Fig. 3 and Supplementary Materials ). Poor reporting of the pieces of essential information within the studies was an issue that was identified at multiple points within this review. This was a key factor in the risk of bias and applicability assessment, as frequently important information that was either missing or ambiguous in its description. Reporting guidelines such as CLAIM and also STARD-AI (currently in development) are useful resources that could help authors to improve the completeness of reporting within their studies 29 , 86 . Greater endorsement and awareness of these guidelines could help to improve the completeness of reporting of this essential information in a study. The consequence of identifying so many studies with areas of concern, means that if the work were to be replicated with these concerns addressed, there is a risk that a lower diagnostic accuracy performance would be found. For this review, with 98–99% of studies containing areas of concern, any results for diagnostic accuracy need to be interpreted with caution. This is concerning due to the risk of undermining confidence of the use of AI tools if real world performance is poorer than expected. In future, greater transparency and reporting of the details of datasets, index test, reference standard and other areas highlighted could help to ameliorate these issues.

Limitations

It must be acknowledged that there is uncertainty in the interpretation of the diagnostic accuracy of the AI models demonstrated in these studies. There was substantial heterogeneity in the study design, metrics used to demonstrate diagnostic accuracy, size of datasets, unit of analysis (e.g. slide, patch, pixel, specimen) and the level of detail given on the process and conduct of the studies. For instance, the total number of WSIs used in the studies for development and testing of AI models ranged from less than ten WSIs to tens of thousands of WSIs 87 , 88 . As discussed, of the 100 papers identified for inclusion in this review, 99% had at least one area at high or uncertain risk of bias or applicability concerns and similarly of the 48 papers included in the meta-analysis, 98% had at least one area at risk. Results for diagnostic accuracy in this paper should therefore be interpreted with caution.

Whilst 100 papers were identified, only 48 studies were included in the meta-analysis due to deficient reporting. Whilst the meta-analysis provided a useful indication of diagnostic accuracy across disease areas, data for true positive, false positive, false negative and true negative was frequently missing and therefore made the assessment more challenging. To address this problem, missing data was requested from authors. Where a multiclass study output was provided, this was combined into a 2 × 2 confusion matrix to reflect disease detection/diagnosis, however this offers a more limited indication of diagnostic accuracy. AI specific reporting guidelines for diagnostic accuracy should help to improve this problem in future 86 .

Diagnostic accuracy in many of the described studies was high. There is likely a risk of publication bias in the studies examined, with studies of similar models with lower reported performance on testing that are likely missing from the literature. AI research is especially at risk of this, given it is currently a fast moving and competitive area. Many studies either used datasets that were not randomly selection or representative of the general patient population, or were unclear in their description of case selection, meaning studies were at risk of selection bias. The majority of studies used either one or two data sources only and therefore the training and test datasets may have been comparatively similar. All of these factors should be considered when interpreting performance.

Conclusions

There are many promising applications for AI models in WSIs to assist the pathologist. This systematic review has outlined a high diagnostic accuracy for AI across multiple disease types. A larger body of evidence is available for gastrointestinal pathology, urological pathology and breast pathology. Many other disease areas are underrepresented and should be explored further in future. To improve the quality of future studies, reporting of sensitivity, specificity and raw data (true positives, false positives, false negatives, true negatives) for pathology AI models would help with transparency in comparing diagnostic performance between studies. Providing a clear outline of the breakdown of data and the data sources used in model development and testing would improve interpretation of results and transparency. Performing an external validation on data from an alternative source to that on which an AI model was trained, providing details on the process for case selection and using large, diverse datasets would help to reduce the risk of bias of these studies. Overall, better quality study design, transparency, reporting quality and addressing substantial areas of bias is needed to improve the evidence quality in pathology AI and to therefore harness the benefits of AI for patients and clinicians.

This systematic review and meta-analysis was conducted in accordance with the guidelines for the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses” extension for diagnostic accuracy studies (PRISMA-DTA) 89 . The protocol for this review is available https://www.crd.york.ac.uk/prospero/display_record.php?ID = CRD42022341864 (Registration: CRD42022341864).

Eligibility criteria

Studies reporting the diagnostic accuracy of AI models applied to WSIs for any disease diagnosed through histopathological assessment and/or immunohistochemistry (IHC) were sought. This included both formalin fixed tissue and frozen sections. The primary outcome was the diagnostic accuracy of AI tools in detecting disease or classifying subtypes of disease. The index test was any AI model applied to WSIs. The reference standard was any diagnostic histopathological interpretation by a pathologist and/or immunohistochemistry.

Studies were excluded where the outcome was a prediction of patient outcomes, treatment response, molecular status, whilst having no detection or classification of disease. Studies of cytology, autopsy and forensics cases were excluded. Studies grading, staging or scoring disease, but without results for detection of disease or classification of disease subtypes were also excluded. Studies examining modalities other than whole slide imaging or studies where WSIs were mixed with other imaging formats were also excluded. Studies examining other techniques such as immunofluorescence were excluded.

Data sources and search strategy

Electronic searches of PubMed, EMBASE and CENTRAL were performed from inception to 20th June 2022. Searches were restricted to English language and human studies. There were no restrictions on the date of publication. The full search strategy is available in Supplementary Note 1 . Citation checking was also conducted.

Two investigators (C.M. and H.F.A.) independently screened titles and abstracts against a predefined algorithm to select studies for full text review. The screening tool is available in Supplementary Note 2 . Disagreement regarding study inclusion was resolved by discussion with a third investigator (D.T.). Full text articles were reviewed by two investigators (C.M. and E.L.C.) to determine studies for final inclusion.

Data extraction and quality assessment

Data collection for each study was performed independently by two reviewers using a predefined electronic data extraction spreadsheet. Every study was reviewed by the first investigator (C.M.) and a team of four investigators were used for second independent review (E.L.C./C.J./G.M./C.C.). Data extraction obtained the study demographics; disease examined; pathological subspecialty; type of AI; type of reference standard; datasets details; split into train/validate/test sets and test statistics to construct 2 × 2 tables of the number of true-positives (TP), false positives (FP), false negatives (FN) and true negatives (TN). An indication of best performance with any diagnostic accuracy metric provided was recorded for all studies. Corresponding authors of the primary research were contacted to obtain missing performance data for inclusion in the meta-analysis.

At the time of writing, the QUADAS-AI tool was still in development and so could not be utilised 90 . Therefore, a tailored QUADAS-2 tool was used to assess the risk of bias and any applicability concerns for the included studies 86 , 91 . Further details of the quality assessment process can be found in Supplementary Note 3 .

Statistical analysis

Data analysis was performed using MetaDTA: Diagnostic Test Accuracy Meta-Analysis v2.01 Shiny App to generate forest plots, summary receiver operating characteristic (SROC) plots and summary sensitivities and specificities, using a bivariate random effects model 92 , 93 . If available, 2 × 2 tables were used to include studies in the meta-analysis to provide an indication of diagnostic accuracy demonstrated in the study. Where unavailable, this data was requested from authors or calculated from other metrics provided. For multiclass tasks where only multiclass data was available, the data was combined into a 2 × 2 confusion matrix (positives and negatives) format to allow inclusion in the meta-analysis. If negative results categories were unavailable for multiclass tasks, (e.g. for multiple comparisons between disease types only) then these had to be excluded. Additionally, mean sensitivity and specificity were examined in the largest pathological subspecialty groups, for cancer vs non-cancer diagnoses and for multiclass vs binary tasks to compare diagnostic accuracy among these studies.

Data availability

All data generated or analysed during this study are included in this published article and its supplementary information files.

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Acknowledgements

C.M., C.J., G.M. and D.T. are funded by the National Pathology Imaging Co-operative (NPIC). NPIC (project no. 104687) is supported by a £50 m investment from the Data to Early Diagnosis and Precision Medicine strand of the Government’s Industrial Strategy Challenge Fund, managed and delivered by UK Research and Innovation (UKRI). E.L.C. is supported by the Medical Research Council (MR/S001530/1) and the Alan Turing Insititute. C.C. is supported by the National Institute for Health and Care Research (NIHR) Leeds Biomedical Research Centre. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care. H.F.-A. is supported by the EXSEL Scholarship Programme at the University of Leeds. We thank the authors who kindly provided additional data for the meta-analysis.

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C.M., E.L.C., D.T. and D.D.S. planned the study. C.M. conducted the searches. Abstracts were screened by C.M. and H.F.A. Full text articles were screened by C.M. and E.L.C. Data extraction was performed by C.M., E.L.C., C.J., G.M. and C.C. CM analysed the data and wrote the manuscript, which was revised by E.L.C., C.J., G.M., C.C., H.F.A., D.D.S. and D.T. All authors approved the manuscript for publication.

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McGenity, C., Clarke, E.L., Jennings, C. et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digit. Med. 7 , 114 (2024). https://doi.org/10.1038/s41746-024-01106-8

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DOI : https://doi.org/10.1038/s41746-024-01106-8

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Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis

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• Jian Yang, 
• Xiaodong Li, 
• Yanting Shi, 
• Kunhong Wang

• Published: May 14, 2024
• https://doi.org/10.1371/journal.pone.0303421
• Reader Comments

Background and aims

Gastric intestinal metaplasia is a precancerous disease, and a timely diagnosis is essential to delay or halt cancer progression. Artificial intelligence (AI) has found widespread application in the field of disease diagnosis. This study aimed to conduct a comprehensive evaluation of AI’s diagnostic accuracy in detecting gastric intestinal metaplasia in endoscopy, compare it to endoscopists’ ability, and explore the main factors affecting AI’s performance.

The study followed the PRISMA-DTA guidelines, and the PubMed, Embase, Web of Science, Cochrane, and IEEE Xplore databases were searched to include relevant studies published by October 2023. We extracted the key features and experimental data of each study and combined the sensitivity and specificity metrics by meta-analysis. We then compared the diagnostic ability of the AI versus the endoscopists using the same test data.

Twelve studies with 11,173 patients were included, demonstrating AI models’ efficacy in diagnosing gastric intestinal metaplasia. The meta-analysis yielded a pooled sensitivity of 94% (95% confidence interval: 0.92–0.96) and specificity of 93% (95% confidence interval: 0.89–0.95). The combined area under the receiver operating characteristics curve was 0.97. The results of meta-regression and subgroup analysis showed that factors such as study design, endoscopy type, number of training images, and algorithm had a significant effect on the diagnostic performance of AI. The AI exhibited a higher diagnostic capacity than endoscopists (sensitivity: 95% vs. 79%).

Conclusions

AI-aided diagnosis of gastric intestinal metaplasia using endoscopy showed high performance and clinical diagnostic value. However, further prospective studies are required to validate these findings.

Citation: Li N, Yang J, Li X, Shi Y, Wang K (2024) Accuracy of artificial intelligence-assisted endoscopy in the diagnosis of gastric intestinal metaplasia: A systematic review and meta-analysis. PLoS ONE 19(5): e0303421. https://doi.org/10.1371/journal.pone.0303421

Editor: Chih-Wei Tseng, Dalin Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, TAIWAN

Received: January 3, 2024; Accepted: April 25, 2024; Published: May 14, 2024

Copyright: © 2024 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AI, Artificial Intelligence; AUC, Area Under the SROC curve; BLI, Blue-Laser Imaging; CI, Confidence Interval; DOR, Diagnostic Odds Ratio; GIM, Gastric Intestinal Metaplasia; LCI, Linked-Color Imaging; ME-NBI, Magnifying Endoscopy with NBI; NBI, Narrow-Band Imaging; NLR, Negative Likelihood Ratio; PLR, Positive Likelihood Ratio; SROC, Summary Receiver Operating Characteristic; WLI, White-Light Imaging

Introduction

Gastric cancer ranks fifth in terms of global cancer prevalence, posing a serious threat to human health [ 1 ]. Although the incidence of gastric cancer has decreased over the past three decades, the absolute number of cases continues to rise due to an aging population and a shift towards younger age groups developing gastric cancer. Hence, reducing the incidence and mortality of gastric cancer remains an urgent issue [ 1 ].

The progression of the most gastric cancers is a cascade pattern, which includes gastritis, atrophic gastritis (AG), intestinal metaplasia, heterogeneous hyperplasia, lastly culminating in cancer [ 2 , 3 ]. AG and gastric intestinal metaplasia (GIM) are important intermediate- and high-risk factors for the development of gastric cancer. Early detection of these lesions is essential for delaying or halting the development of gastric cancer. In clinical practice, white light endoscopy is typically used to observe gastric lesions. However, studies have shown that the correlation between histology and general white light endoscopy diagnosis is low [ 4 – 8 ].

In the last decade, artificial intelligence (AI) has garnered significant attention within the scientific community, leading to considerable research being conducted on AI-related subjects, such as neural networks, machine learning, and deep learning. It has been used in various industries to provide powerful solutions to complex problems [ 9 – 11 ]. Computer vision is an important research area in AI. By applying various algorithms, computer vision systems can analyze and extract meaningful information from images or videos. Image classification algorithms are used to identify the category to which an image belongs, represented by VGG [ 12 ], ResNet [ 13 ], TResNet [ 14 ], SE-ResNet [ 15 ], and EfficientNet [ 16 ]. Object detection algorithms focus on finding one or more targets in an image and framing them with rectangular boxes; typical algorithms are SSD [ 17 ], YOLO [ 18 , 19 ], and R-CNN [ 20 ]. Semantic segmentation algorithms identify each pixel in the image and is capable of accurate segmentation based on the boundary of the target; typical algorithms are UNet++ [ 21 ], DeepLab [ 22 , 23 ], and BiSeNet [ 24 ]. These techniques are widely used in medical imaging diagnoses [ 25 , 26 ].

In gastrointestinal endoscopy, AI has been used to diagnose various diseases [ 27 – 29 ] and has achieved good diagnostic efficacy. Bang et al. [ 30 ] performed a meta-analysis including eight studies that specifically examined the accuracy of AI-assisted endoscopy in the diagnosis of Helicobacter pylori infection. Our previous study [ 31 ] conducted a meta-analysis on the accuracy of AI-assisted endoscopy in diagnosing chronic atrophic gastritis. In this study, we utilized meta-analysis to evaluate the accuracy of AI in diagnosing GIM, explored the main factors affecting AI’s ability, and compared AI performance with that of endoscopists, thereby providing an objective basis for applying AI in clinical diagnosis.

Before commencing the study, we had registered it with PROSPERO [ 32 ] (ID: CRD42022378974). This study strictly followed the PRISMA-DTA [ 33 ] guidelines. The associated checklist for PRISMA-DTA can be found in S1 Table .

Searching strategy

To obtain relevant studies, we searched the following five databases from their establishment up to October 2023: PubMed, Embase, Web of Science, Cochrane Library, and IEEE Xplore. Notably, PubMed, Embase and Cochrane Library are common medical databases, while the Web of Science is an extensive and comprehensive database. The IEEE Xplore database covers computer science, electronics, and other related fields. Related search terms include: artificial intelligence, deep learning, machine learning, computer-aided diagnosis, neural networks, gastritis, gastric precancerous, gastric tissue, and intestinal metaplasia. The detailed search strategy is shown in S2 Table .

Study selection

Inclusion criteria: (a) Studies use AI technology to analyze endoscopic images/videos to detect GIM lesions. (b) Ability to extract 2x2 table data from articles. (c) Clear presentation of diagnostic criteria. (d) A clear description of the AI algorithm and the process of diagnosing GIM. (e) The most recent studies from multiple studies on the same research group, if the AI model or study cohort was the same. Exclusion criteria: (a) Studies in which the full text was unavailable. (b) Studies in which complete four-grid table data were unavailable. (c) Reviews, meta-analyses, editorial reviews, letters to the editor, conference abstracts, and other types of literature. Two authors (J.Y. and X.L.) independently evaluated the search results, and any disagreements were resolved through discussion.

Data extraction

The key data we extracted from each study included the first author, publication year, country/region, study design, study center, diagnostic criteria, algorithm, number of training set samples, test set type, number of test set samples, and 2x2 table data. Two authors (Y.S. and X.L.) independently extracted the data by reading the full text, and disagreements were resolved through discussion.

Quality assessment

QUADAS-2 [ 34 ] is the widely used quality assessment tool for diagnostic accuracy studies, and includes four parts: Patient Selection, Index Testing, Reference Standards, and Flow and Timing. However, QUADAS-2 is not fully applicable to AI-centered diagnostic accuracy studies [ 35 , 36 ]; therefore, we supplemented QUADAS-2 to make it more suitable for AI-centered studies. In the patient selection section, the source, size, and quality of the input data were accurately described. In index testing, whether the AI model is tested using an independent test set. In the reference standard section, whether pathological tissue biopsies were used as the “Gold Standard” is described.

Statistical analysis

Based on a bivariate mixed-effects model, we calculated diagnostic performance indicators such as combined sensitivity, specificity, and diagnostic odds ratio (DOR). The likelihood ratio is a composite index that reflects sensitivity and specificity. The positive likelihood ratio (PLR) > 10 and the negative likelihood ratio (NLR) < 0.1 indicate high diagnostic performance. The area under the curve (AUC) and DOR are comprehensive measures to evaluate diagnostic accuracy. AUC> = 0.9 indicates the high accuracy of the diagnostic test. A larger DOR value indicated a better diagnostic performance.

The heterogeneity of the studies was assessed by the visual inspection of summary receiver operating characteristic and forest plots and counted by the I 2 value. If heterogeneity existed, a subgroup analysis and meta-regression were performed. The clinical applicability was assessed using Fagan plots. Deek’s funnel plot assessed publication bias, and when the angle between the straight line in the plot and the coordinate X-axis was closer to 90°, it indicated the existence of publication bias. When P<0.05 was statistically significant, publication bias was present.

Quality assessment of the included studies was performed using Review Manager 5.4 (Cochrane Collaboration, Oxford, UK). Other statistical analyses and graphing were conducted using Stata/SE16.0 (Stata, TX, USA).

Included studies

The final search was conducted on October 12, 2023, yielding 637 papers. Among these, 228 duplicates were automatically removed through EndNote, 381 irrelevant papers were excluded by reading the titles and abstracts, two were excluded without retrieving the full text, and 14 were excluded after examining the full text. Twelve studies [ 37 – 48 ] ( Table 1 ) were finally included. The flow diagram for study selection is shown in Fig 1 .

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https://doi.org/10.1371/journal.pone.0303421.g001

https://doi.org/10.1371/journal.pone.0303421.t001

Study characteristics

Basic information of the 12 studies are shown in Table 1 , and the participant characteristics of each study are shown in S3 Table . Among the 12 studies, three were prospective [ 37 , 39 , 42 ], and nine were retrospective; three studies [ 37 – 39 ] were multi-center and nine were single-center; three studies [ 44 , 46 , 47 ] used expert consensus as the diagnostic criterion and nine used pathological findings as the diagnostic criterion; three studies [ 37 , 38 , 43 ] used only plain white light imaging (WLI) model, and nine studies involved narrow-band imaging (NBI), magnified endoscopy with NBI (ME-NBI), blue laser imaging, and linked color imaging (LCI) model.

Mu et al. [ 37 ] developed a computer-aided system to identify non-gastritis, common gastritis, AG, and GIM. The system contains five deep learning models. ResNet was used for lesion classification and UNet++ network was used for lesion segmentation. We extracted only the diagnostic data for GIM.

Lin et al. [ 38 ] collected 7,037 WLI images and corresponding biopsy information from 14 hospitals. The images were classified into three categories: AG, non-AG, and GIM, based on pathological findings. The AI algorithm was TResNet.The sensitivity and specificity of the AI model to diagnose GIM were 97.9% and 97.5%.

Xu et al. [ 39 ] collected WLI, ME-NBI, and blue laser imaging (BLI) images from five hospitals for model training to identify AG and GIM. The models were tested on internal, external, and prospective video test sets. The diagnostic data were collected from a randomly selected prospective video test set.

Yang et al. [ 40 ] constructed a dataset containing 21,420 WLI and LCI images to train a AI model for recognizing AG and GIM. The authors propose a dual transfer learning strategy to improve the model’s performanc. We extracted the data of the AI model on the WLI-independent and LCI-independent test sets and then combined them.

Yan et al. [ 41 ] collected 2,357 NBI and ME-NBI images from 416 patients for training an AI model for recognizing GIM. The sensitivity, specificity, and accuracy of the model were 91.9%, 86.0%, and 88.8%, respectively. Although the AI models performed better than the human experts, there was no significant difference between them. We combined the test results of the AI model on the NBI set and the ME-NBI set.

Siripoppohn et al. [ 42 ] implemented semantic segmentation of GIM by adding three additional preprocessing techniques to the BiSeNet network and compared it with the classical semantic segmentation algorithms, DeepLabV3+ and U-Net. Diagnostic data were extracted from the improved algorithm using a prospective video test set.

Huang et al. [ 43 ] constructed custom neural networks for identifying lesions, such as H. pylori infection, atrophy, and GIM, and extracted data relevant to identifying GIM. Although the study prospectively selected 104 patients, the model was trained and tested based on the images of these patients; therefore, we considered this to be a retrospective study.

Li et al. [ 44 ] proposed a novel multi-feature fusion method to identify GIM, which extracts features from pixels, colors, and textures of endoscopic images, respectively. The authors trained and tested the model using 1,050 images and achieved an accuracy of 90.28%.

Wong et al. [ 45 ] proposed a novel broad-learning system stacking framework with multiscale attention. This method could reliably diagnose GIM with an accuracy of 93.2%. The authors also compared the AI model’s diagnostic capability to that of endoscopists, and the result showed that AI was competitive with that of skilled endoscopists.

Lai et al. [ 46 ] proposed a multi-scale multi-instance multi-feature joint-learning broad network. The network considers multiple features of each instance at multiple scales, resulting in more accurate classification. By training on a limited dataset, the network recognizes GIM with an accuracy of 85%.

Li et al. [ 47 ] proposed a combination of conventional and deep learning methods for IM lesion area localization and offset generation. The method could recognize the severity of GIM with an accuracy of 84.17%.

Pornvoraphat et al. [ 48 ] utilized AI techniques to achieve real-time segmentation of GIM. The AI algorithm is based on BiSeNet, and the authors used techniques such as negative sampling and label smoothing to improve the model’s performance. The sensitivity, specificity and accuracy of the AI model were 91%, 96% and 96%, respectively.

The quality was assessed using the Supplemented QUADAS-2 tool ( Fig 2 ). In the patient selection section, three studies were of unknown risk. One study [ 40 ] did not state the source of the patients, while two studies [ 47 , 48 ] did not state the number of patients included. In the reference standards section, three studies [ 44 , 46 , 47 ] used expert consensus rather than pathological findings as the gold standard and were considered high risk.

https://doi.org/10.1371/journal.pone.0303421.g002

Meta-analysis results

We imported data from 12 studies into Stata/SE 16.0 for meta-analysis. The pooled sensitivity and specificity of AI diagnosing GIM were 94% (95% CI: 0.92–0.96, I 2 = 43.71%) and 93% (95% CI: 0.89–0.95, I 2 = 84.78%), respectively ( Fig 3 ). The PLR and NLR were 12.59 (95% CI: 0.38–18.91) and 0.06 (95% CI: 0.05–0.09), respectively ( S1 Fig ). The DOR ( S2 Fig ) and AUC ( Fig 4 ) were 201.5 (95% CI: 110.18–368.51) and 0.97 (95% CI: 0.97–0.98), respectively. With a PLR (12.59) greater than 10, it suggested that AI had the capability to confirm the diagnosis of GIM. The NLR value (0.06<0.1) indicates that AI can reliably exclude GIM. The DOR value (217>1) indicated a better discriminative effect of this diagnostic test, and an AUC (0.97) closer to 1 indicated a better diagnostic effect. It is important to note that the I 2 of combined sensitivity and specificity suggest a high degree of heterogeneity between studies.

https://doi.org/10.1371/journal.pone.0303421.g003

https://doi.org/10.1371/journal.pone.0303421.g004

Fagan plots were drawn to evaluate the clinical applicability of AI ( Fig 5 ). With a pre-test probability set at 0.5, a positive AI diagnostic result indicated a 93% probability of the patient having GIM, while a negative result suggested a 6% likelihood, confirming or excluding the presence of Gastrointestinal Intestinal Metaplasia (GIM).

https://doi.org/10.1371/journal.pone.0303421.g005

Subgroup analysis

We conducted subgroup analyses to investigate the impact of various factors on the performance of (AI) in diagnosing GIM. The factors included study design (prospective or retrospective), study center (multi-center or single-center), endoscopy type (WLI only or others), number of training images (>1500 or <1500), and algorithm type (classification algorithm or others) ( Table 2 ).

https://doi.org/10.1371/journal.pone.0303421.t002

The effect of study center on sensitivity was statistically significant, and the effect of other grouping conditions on sensitivity was extremely significant. The algorithm type had a significant effect on specificity. All of the above factors could be potential sources of heterogeneity between studies.

Publication bias and sensitivity analysis

To assess the presence of publication bias, we performed a Deeks’ funnel plot asymmetry test ( Fig 6 ). The P value was 0.15, indicating no significant publication bias.

https://doi.org/10.1371/journal.pone.0303421.g006

To delve deeper into the heterogeneity among studies, we conducted a pooled analysis by systematically excluding each study one at a time. After removing the studies by Pornvoraphat [ 48 ], the most significant changes were observed in combined sensitivity and specificity, which were found to be 95% (95% CI: 0.93–0.96, I 2 = 40.28%) and 92% (95% CI: 0.88–0.95, I 2 = 83.04%), respectively. However, this is not significantly different from the original results, indicating that the meta-analysis results were relatively stable.

AI vs. endoscopists

To further explore the diagnostic ability of the AI, we compared it to that of the endoscopists. We collected 5 sets of data from 12 studies ( Table 1 ) for the meta-analysis. An essential criterion for data extraction was that the test sets used by the AI and endoscopists must be identical. The comparison results are shown in Table 3 . Their specificities showed no significant disparity, and while AI exhibited a superior sensitivity compared to the endoscopists, this variance did not reach statistical significance.

https://doi.org/10.1371/journal.pone.0303421.t003

Dilaghi et al. [ 49 ] conducted a meta-analysis on AI’s role in the diagnosis of precancerous gastric lesions and Helicobacter pylori infection, with two studies involving the diagnosis of GIM. To the best of our knowledge, this is the first systematic review and meta-analysis focusing on the diagnosis of GIM using AI. This meta-analysis included 12 studies involving 11,173 relevant patients and 46,268 images/videos. In addition to pooling the diagnostic performance of AI, we explored the impact of factors such as different algorithms, varied image quantities, etc., on AI performance. Furthermore, we compared the diagnostic abilities of endoscopists with those of AI. The results demonstrate that various indicators of AI-assisted diagnosis for GIM exhibit satisfactory levels. This indicates that AI can help doctors diagnose GIM more accurately, thus reducing the rate of missed diagnoses and misdiagnoses. In addition, AI can accelerate the diagnostic process, which reduces doctors’ workload and improves their efficiency.

There are still some limitations of this study: (a) The diagnostic value of AI algorithms may not be adequately assessed due to the relatively small number of studies and limited sample size. (b) The 12 included studies were conducted in Asia, and the results of the meta-analysis may not apply to a wider population. (c) Heterogeneity among the studies was very high. Although subgroup analyses were conducted, the restricted number of studies did not allow for further analysis of influencing factors, such as the type of test set (image or video) and specific endoscope type (e.g., NBI or LCI). (d) Most studies have identified only intestinal metaplasia and atrophic gastritis, and further validation is needed to determine whether other lesions affect the determination of AI. (e) Most studies were retrospective, and the test set included static images. More prospective, real-time, endoscopic-video-based studies are required to validate whether AI can be adapted to complex endoscopic environments.

Among the twelve studies, one [ 47 ] employed AI to identify GIM and assess its severity through endoscopic image analysis. This process is crucial for accurately pinpointing the most representative lesion area for biopsy, indicating a significant avenue for future research. Additionally, identifying early malignant changes from GIM remains a challenge. Previous studies have used AI to identify early gastric cancer by analyzing WLI, NBI, or ME-NBI images [ 50 – 52 ]. Ikenoyama et al. [ 51 ] used conventional WLI images to identify early cancers smaller than 20mm, with AI sensitivity and specificity of 58.4% and 87.3%, respectively. While these results are encouraging, AI performance still needs to be improved.

Most included studies used deep learning techniques, but none explained AI’s decision-making process in detail. Due to their complexity and "black-box" nature, deep learning models often find it difficult to explain their internal working mechanisms and decision-making basis, which largely limits their clinical applications [ 53 ]. The introduction of algorithms such as LRP [ 54 ] and Grad-CAM [ 55 ] has played an important role in enhancing the explainability of existing deep learning models [ 56 , 57 ]. Developing inherently explainable AI models will enable better application in clinical practice.

All the included studies were tested using their own datasets, and it was difficult to directly compare the performances of the models. High-quality publicly available datasets can be used as evaluation benchmarks to compare the performances of different algorithms. Additionally, publicly available datasets can encourage more people to participate in AI research. Currently, publicly available gastrointestinal image datasets include Hyper-Kvasir [ 58 ] and SUN-SEG [ 59 ]; however, there is a lack of large publicly available image datasets related to GIM.

It is worth noting that although AI applied to healthcare has made many technological breakthroughs, it also poses certain challenges to the current value system and legal system from the legal and ethical levels. For example, it may raise privacy and data security issues and legal liability issues when AI’s decisions are made incorrectly. As AI continues to advance, collaborative efforts among governments, healthcare organizations, and AI technology companies are crucial to establishing a robust framework that ensures the responsible and fal deployment of AI in clinical settings.

The pooled sensitivity of our meta-analysis was 94% (95% confidence interval: 0.92–0.96) and specificity was 93% (95% confidence interval: 0.89–0.95). Comparisons by AI vs. endoscopists showed that AI had a higher sensitivity (95% vs. 79%). The results show that AI performed excellently in diagnosing GIM, which provides an evidence-based support for the clinical application of AI. At the same time, we identified some potential limitations, such as the quality of the dataset, generalizability of the AI model, and explainable AI. The application of AI-assisted endoscopy in the medical field is promising. Future research could focus on prospective studies, improvement of the explainability of models, and adaptation to different patient characteristics.

Supporting information

S1 table. prisma-dta checklist..

https://doi.org/10.1371/journal.pone.0303421.s001

S2 Table. Searching strategy to find relevant articles.

https://doi.org/10.1371/journal.pone.0303421.s002

S3 Table. Participant characteristics and algorithmic details of included studies.

https://doi.org/10.1371/journal.pone.0303421.s003

S1 Fig. Forest plot of PLR and NLR of AI in identifying GIM.

https://doi.org/10.1371/journal.pone.0303421.s004

S2 Fig. Forest plot for the diagnostic odds ratio and diagnostic score after combination.

https://doi.org/10.1371/journal.pone.0303421.s005

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Evaluation of artificial intelligence techniques in disease diagnosis and prediction

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A broad range of medical diagnoses is based on analyzing disease images obtained through high-tech digital devices. The application of artificial intelligence (AI) in the assessment of medical images has led to accurate evaluations being performed automatically, which in turn has reduced the workload of physicians, decreased errors and times in diagnosis, and improved performance in the prediction and detection of various diseases. AI techniques based on medical image processing are an essential area of research that uses advanced computer algorithms for prediction, diagnosis, and treatment planning, leading to a remarkable impact on decision-making procedures. Machine Learning (ML) and Deep Learning (DL) as advanced AI techniques are two main subfields applied in the healthcare system to diagnose diseases, discover medication, and identify patient risk factors. The advancement of electronic medical records and big data technologies in recent years has accompanied the success of ML and DL algorithms. ML includes neural networks and fuzzy logic algorithms with various applications in automating forecasting and diagnosis processes. DL algorithm is an ML technique that does not rely on expert feature extraction, unlike classical neural network algorithms. DL algorithms with high-performance calculations give promising results in medical image analysis, such as fusion, segmentation, recording, and classification. Support Vector Machine (SVM) as an ML method and Convolutional Neural Network (CNN) as a DL method is usually the most widely used techniques for analyzing and diagnosing diseases. This review study aims to cover recent AI techniques in diagnosing and predicting numerous diseases such as cancers, heart, lung, skin, genetic, and neural disorders, which perform more precisely compared to specialists without human error. Also, AI's existing challenges and limitations in the medical area are discussed and highlighted.

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1 Introduction

Advances in emerging computer-based technologies are overgrowing. Digital healthcare offers numerous opportunities to reduce human error, improve clinical outcomes, and track data over time. Artificial Intelligence (AI) methods, including Machine Learning (ML) and Deep Learning (DL) algorithms, are widely used in the prediction and diagnosis of several diseases, especially those whose diagnosis is based on imaging or signaling analysis [ 1 , 2 ]. AI can also help to identify demographics or environmental areas where disease or high-risk behaviors are prevalent [ 3 , 4 ]. ML techniques have achieved significant success in medical image analysis due to the advanced algorithms that enable the automated extraction of improved features [ 5 , 6 ]. ML is based on learning methods and can be divided into three categories: supervised (classification, regression, and composition), unsupervised (association, clustering, and dimensionality), and reinforced learning [ 7 ] (Fig.  1 ).

Machine Learning models and main algorithms

Several computations and operations on input data are performed through ML algorithms. Data preprocessing is the first and essential step to reducing false predictions or incorrect results, speeding up the data processing, and eventually improving overall data quality. After data preprocessing, crucial features are extracted and implemented according to the selected ML or DL model for image classification. Feature selection reduces dimension and boosts algorithm performance [ 8 ]. Model training and parameter adjustment are also performed based on the chosen algorithm through data processing to make accurate decisions and obtain reasonable classifications or predictions in the last phase, Fig.  2 .

The classification process phases in medical image analysis

ML enables computers to perform the tasks of medical professionals. ML has a widely used subfield in medical image recognition called deep learning (DL). DL is a method for designing the ML algorithm in which simple concepts are built on top of each other to form a deep structure with numerous processing layers. In other words, DL is the development of ML for analyzing massive data [ 9 , 10 , 11 ]. It replaces the classical manual method of designing and extracting patterns used for classification with an automated strategy that allows a computer to decide which features are essential by training on a dataset. While DL is not a new concept, processing massive data and increased computing power have made DL successful and popular in recent years.

DL has outperformed previous advanced algorithms in several visual recognition tasks, and its performance has improved significantly. ImageNet Large Scale Visual Recognition Challenge [ 12 ] is annually the largest object recognition competition. Researchers classified the 1.3 million high-resolution images using the DL-based CNN model [ 13 ]. They dramatically improved their model performance by achieving error rates of 39.7% and 18.9% and winning the challenge. DL algorithms have become more popular since then. A DL algorithm is a deep artificial neural network (ANN) inspired by human brain cells that consist of several simple processing units that combine to form a more complex architecture. These units are grouped in layers in every algorithm and referred to as nerve cells, where the input signals are combined and transferred to other cells if their value is higher than the threshold value. In the synthetic type, they are replaced by a sum and an activation function that combine to create more complex relationships, similar to the human brain, through the network.

A convolution neural network (CNN), as a successful approach for image analysis and classification, is a supervised DL model. CNN consists of fully connected layers with standard weights that lead to fewer parameters for training features through the backpropagation process. They are designed to extract spatial information from input images. CNN aims to learn hierarchical features adaptively, classify image data, and extract their features automatically, as demonstrated in Fig.  3 . The essential advantage of this algorithm is learning very abstract features with few parameters and simple preprocessing.

The DL model (CNN) structure for classification of brain disease using brain CT scan images as input data

The neural network's initialization and the samples' order during the training phase are usually random. However, when the training is over, there is nothing unexpected with the neural network. It involves well-defined calculations, but its complex and profound structure often make it incomprehensible to humans. Therefore, the training method is usually not mentioned to explain the behavior of networks. Typically, a trained network can be understood by the characteristics of the data set used for training.

A large amount of data is processed in the healthcare system, making it a suitable platform for developing successful algorithms that can be exploited using DL or ML approaches. While many different data types are used for medical evaluations, images are a broad data type that makes medical analytics potentially very tangible. Medical images are created in other forms using various equipment, such as ultrasound, X-rays, computed tomography, magnetic resonance imaging, microscopy, and scintigraphy. All these techniques can create diverse images, but all images have the potential for auto-analysis through AI algorithms to investigate and predict various diseases. To understand how each AI-based model helps diagnose and predict disease, examining the application of multiple algorithms is essential [ 14 , 15 ].

Recently, ML and DL techniques have established an advanced approach to emerging techniques in computer-based diagnosis, which have been widely conducted in various medical fields to diagnose or predict multiple diseases [ 16 , 17 , 18 , 19 ]. These methods have led to more accurate diagnoses and increased efficiency [ 16 , 17 ]. This paper reviews the ML and DL models for diagnosing various diseases.

2 Related works

AI has recently undergone significant advances that have achieved much attention from numerous companies and academic fields. The most successful technique is driven by advances in ANNs, called Deep Learning (DL), a set of processes and algorithms that automatically enable computers to detect complex patterns in large datasets. Feeding these advances is increased access to data (“big data”), user-friendly software frameworks, and an explosion of existing computing power that allows deep neural networks to be widely used. DL became prominent in image processing when neural networks performed better than other methods in several high-resolution image analysis criteria.

In the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) [ 12 ], a CNN model reduced the second-highest error rate in image classification work by 50% in 2012. Before that, computers were thought to be very difficult to detect objects in natural images. So far, CNN has even surpassed human performance in ILSVRC to the point where the task of classifying ILSVRC is essentially solved. DL techniques have become the objective standard solution for various computer vision problems. Numerous studies have suggested the use of DL techniques in the diagnosis of acute human diseases.

Researchers have used multiple scenarios based on ML and DL models to predict conditions such as liver disease, heart disease, Alzheimer's disease, and various types of cancers for which early detection is vital for treating [ 20 , 21 , 22 ]. Some researchers have used DL techniques to diagnose and differentiate bacterial pneumonia using pediatric chest radiographs [ 23 , 24 ]. Significant efforts have also been made to identify the different features of chest CT imaging characteristics of various diseases [ 25 , 26 ]. New hybrid models based on Case-Based Reasoning were proposed to diagnose various skin diseases in different studies [ 27 , 28 ]. The model’s output as an application could diagnose multiple skin diseases and suggest proper treatment. Proposing personalized real-time monitoring systems based on ANN techniques to receive vital information about the body is widely used in healthcare. This device can help patients manage their health, especially in critical conditions [ 29 ]. Researchers in [ 30 ] applied ANN models for predicting diabetes disease and achieved 91% accuracy.

ML classification methods are implemented to analyze data automatically for the diagnosis or prediction of various diseases. Researchers developed a personalized real-time web-based healthcare monitoring system to receive vital information from the body, such as heart rate or blood pressure. This device can help patients manage their health, especially in critical conditions [ 31 ].

AI approaches combined with the Internet of Things (IoT) method in the healthcare system can upgrade treatment procedures and healthcare technology. A reliable IoT-based system using ML algorithms for healthcare was proposed to monitor human activities and the surrounding environment through the body sensor network, BSN-Care [ 32 ]. Another study suggested a hybrid IoT model using a healthcare monitoring system and the Random Forest technique to predict type 2 diabetes (T2D) [ 33 ]. Researchers also investigated the risk of T2D among people based on their personal lifestyle information and achieved high accuracy using the random forest classifier, which outperformed other algorithms [ 34 ]. A mobile-based platform was developed for real-time tuberculosis disease (TD) antigen-specific antibody detection using the random forest classifier and gained 98.4% accuracy [ 35 ]. A research study proposed an AI-based framework for classifying multiple gastrointestinal (GI) diseases using RNN and LSTM networks and achieved 97.057% accuracy [ 14 ].

Hypertension healthcare control and awareness are the two most critical points to reducing stroke and cardiovascular disease. Researchers assessed digital healthcare technologies and AI in this regard and suggested a privacy protection system to collect and store individuals' data [ 36 ]. Furthermore, many researchers have done several studies on disease prediction to recognize and predict them in their early stages. A novel hybrid ML model was proposed based on the IoT for detection in the initial phase of diseases with an accuracy of 100% and a precision of 99.50% [ 37 ]. In another work, researchers have proposed an approach to predict cardiovascular disease according to various features. They used a hybrid random forest classifier and gained 88.7% accuracy [ 38 ].

A research study on detecting positive urine culture proposed an ML algorithm, XGBoost, to accurately diagnose results [ 39 ]. This model outperformed other developed models, and its accuracy ranged from 0.826 to 0.904. Another study used the CNN model for feature extraction in malaria-infected blood cell images [ 40 ]. Another work also predicted malaria infection using an ML model [ 41 ]. Researchers used different ML algorithms such as Random Forest, Support Vector Machine, Logistic Regression, K-Nearest Neighbor, and Naïve Bayes to identify acute exacerbations in chronic obstructive pulmonary disease. They found the SVM model achieved the best performance [ 42 ]. In other research work, scientists used three ML algorithms, including ANN, distributed random forest, and gradient boosting, to predict opioid abuse in adolescents based on the 2015–2017 National Survey on Drug Use and Health data. The prediction performance for the area under the receiver operating characteristic curve (AUROC) values ranges from 0.809 to 0.815 [ 43 ]. Likewise, other researchers used multiple ML algorithms, such as CNN, RF, SVM, DT, and AdaBoost classifiers, to propose a model for detecting COVID-19 from an X-ray image dataset and achieved a result of 98.91% accuracy [ 44 ]. ML and DL techniques can be used to detect stress levels in individuals. One approach is to use physiological signals, such as heart rate or respiration, to detect stress. For example, in an extensive study, authors examined different ML models for stress levels based on heart rate variability [ 45 ]. In this work, ML Random Forest outperformed other methods. Other researchers used various ML models to predict diabetes. In their work, Logistic Regression and Support Vector Machines performed well [ 46 ]. In a comprehensive study, researchers used different ML models such as KNN, SVM, ANN, Decision Tree, Logistic Regression, Naïve Bayes, Random Forest, and XGBoost to predict the risk of chronic type 2 diabetes [ 47 ]. In this study, the Random Forest model overpassed the other models with 0.91AUC. Recently, an extended DL model called 3DCellSeg provided powerful performance for analyzing and separating image-based diseases compared to basic models. This DL method has a lightweight deep CNN and requires only one hyperparameter [ 48 ].

3 Artificial intelligence techniques in disease diagnosis and prediction

AI is a vast area merged into various fields of mathematics and science. Everything a machine can do automatically that is considered "intelligence" would be a subset of AI [ 49 ]. AI algorithms are taught on population representation information [ 50 , 51 ]. One of the most important subfields of AI is ML, and the essential subfields of ML are Neural Networks and DL (Fig.  4 ).

Relationship between AI, ML, NN, and DL approach

The ML’s goal is that the machine can train itself based on input data set, experience, and receiving information from feedback [ 52 ]. The ML algorithm optimizes itself based on the information received from the feedback to be as accurate as possible in a particular task. Ideally, the ultimate goal is that it should work accurately on new unseen data sets as well [ 53 ].

Imaging source in the medical area is one of the most widely-used tools for diagnostic patient information. Still, it relies on human interpretation and is subject to increasing resource challenges. Automatic diagnosis of medical imaging through AI, especially in the field of DL, has effectively solved the problems of human error caused by inaccuracy or lack of sufficient experience. AI also plays a crucial role in image-based disease classifications, computer-aided diagnosis (CAD), and image disease segmentation. Since tissues and organ images in the healthcare system cannot be accurately simulated with simple equations, diagnosis tasks in medical imaging need to be learned through a training process.

Detection of any disease and prevention of its spread requires continuous checking and reviewing of data. Prompt action based on accurate data has a significant social and financial impact on the lives of people around the world [ 51 ]. The use of AI in healthcare has improved the collection and processing of valuable data and, at higher levels, the programming of surgical robots [ 54 ]. AI describes a machine's power to study how a human learns by image recognition and pattern recognition in a problematic situation. AI in health care has changed how information is collected, analyzed, and developed for patient care [ 55 ].

3.1 Machine learning application in diagnosis image-based diseases

ML algorithms have many applications in various fields [ 56 , 57 , 58 ]. As a subfield of AI in medical imaging analysis, ML is a promising and growing field. ML has broad applications in computer vision, computer-aided diagnosis, and image processing in detecting diseases [ 59 ]. As medical imaging has advanced with the introduction of new imaging techniques such as multiple incision CT, positron emission tomography, tomosynthesis, magnetic resonance imaging, tomography, and diffuse optical tomography, progressive ML methods are increasingly needed for medical imaging analysis. ML consists of a set of plans for automatically detecting patterns in data and then using those methods to predict future data or make decisions in uncertain situations. The most distinctive feature of ML is that it is data-driven, with limited human participation in the decision-making process. The program learns by analyzing training data and making predictions when new data is entered [ 60 ].

Several recent techniques in ML have been applied to predict or diagnose diseases [ 61 , 62 ]. Natural Language Processing (NLP) techniques have been used to analyze electronic health records (EHRs) to extract information that can be used to predict or diagnose diseases [ 63 ]. Explainable AI techniques, such as SHAP (SHapley Additive exPlanations), are presented to interpret the predictions made by ML models, which is essential in a medical context where the decision-making should be transparent [ 64 ]. Generative models such as GANs (Generative Adversarial Networks) are invented to generate synthetic medical images that can augment existing data, such as lung disease, and improve the result performance [ 65 ]. These techniques are not mutually exclusive and can be combined to improve the model's performance. The technique selection depends on the data type and the specific problem [ 66 ].

3.2 Deep learning applications in diagnosis image-based diseases

DL is the most powerful technology that can automatically learn several features and patterns, making itself one of the most vigorous techniques. DL has made it possible to build predictive models for the early diagnosis of diseases. As scientists use proven pattern analysis methods, DL algorithms perform better than traditional ML methods because of the highly accurate results, automatic feature extraction, and massive data analysis. When it comes to big data, the results of using DL algorithms show a clear advantage over ML. Moreover, the predictive performance of DL often surpasses humans to recommend DL as the preferred method for dealing with images [ 67 ]. DL has received exceptional articulation in the medical field regarding image processing because the diagnosis primarily focuses on extracting useful information from images. In medical image-based diagnosis, DL algorithms are mainly of various types, including CNN, Deep Neural Network (DNN), Deep Belief Network (DBN), Deep Automatic Encoder, Deep Boltzmann Machine (DBM), Deep Intense Normal Machine Learning (DC-ELM), recursive neural network (RNN), and their types such as BLSTM, MDLATM [ 68 ]. Also, RAGCN (Region Aggregation Graph Convolutional Network) is a DL technique for analyzing medical data that utilizes graph convolutional networks (GCNs) to aggregate information from different regions of an image. It is specifically designed for medical images, such as CT and MRI scans, which often have multiple regions of interest (ROIs) that need to be analyzed separately. RAGCN uses a graph-based approach to segment the image into different regions and then applies GCNs to each part to extract features and make predictions. The authors in [ 69 ] presented an automatic bone age estimation method using CNN and GCN. They used CNN and GCN for feature extraction and inference of bone key regions, respectively. By combining these two types of networks, they were able to design a new GCN (RAGCN) that can investigate the features of the region in bone age assessment.

Lesion-attention pyramid network (LAPNet) is another DL medical data method designed to detect and classify lesions in medical images. LAPNet uses a pyramid-based architecture to extract features from the image at different scales. It also uses an attention mechanism to focus on specific regions of the image that are likely to contain lesions; authors in [ 70 ] used this technique to grade diabetic retinopathy. They trained LAPNet on a large dataset of medical images to learn to detect lesion regions.

These are some examples of the various DL techniques that are being used to predict or diagnose diseases. It is important to note that the field of DL is constantly evolving, and new techniques are being developed or combined all the time.

4 Discussion

4.1 the effective role of ai technologies in identifying and predicting human disorders and diseases.

AI technologies are increasingly used in medicine to succeed and gain more accurate knowledge about dangerous disorders and diseases [ 71 , 72 ]. Since AI can interact constructively with image data in the medical world, it is increasingly used in disease diagnosis and prediction [ 73 ].

Learning algorithms and big data derived from medical records or wearable devices are the two most vital tools to implement AI methods efficiently in the health care system to improve disease diagnosis, disease classification, decision-making processes activities, walking aids performance, providing optimal treatment choices, and ultimately helping people to live safer and more prolonged. AI is used to enhance medical analysis and diagnosis in a short time [ 74 , 75 ]. For instance, this technology can detect dangerous tumors in medical images, allowing pathologists to diagnose the disease in the early stage and treat it instead of sending tissues or lesions samples to a lab for long-term investigation [ 76 ]. AI-based algorithms are an effective tool for identifying undiagnosed or less-diagnosed patients, unencoded, and rare diseases. Thus, AI models for disease diagnosis provide ample opportunity for early diagnosis of patients [ 76 ].

The application of ML and DL techniques to diagnose heart diseases is increasing significantly. Due to the existence of a wide range of medical imaging methods, such as CT, ECG, and echocardiography in cardiology, DL can be used accurately and advanced in the analysis and review of cardiovascular data [ 77 , 78 , 79 ]. Coronary atherosclerotic heart disease is a common cardiovascular disease that causes disabilities and severe morbidities. Early diagnosis of this disease is highly effective and has an impressive impact on treatment. In this term, ML and DL methods have achieved remarkable progress in coronary atherosclerotic heart disease diagnosis [ 80 ]. For instance, CT-Fractional Flow Reserve (CT-FFR) based on ML can simplify the processing of diagnosis and reduce times, which would be a powerful tool for predicting major adverse cardiac events [ 81 , 82 ]. Also, CT-FFR based on the DL can simplify the computation, reduce time, and enhance prediction [ 83 , 84 ]. Researchers have used SVM and ANN methods for the early diagnosis of various heart diseases in 170 patients [ 85 ]. They examined Arrhythmia, Cardiomyopathy, CHD, and CAD through SVM and ANN models. The SVM algorithm resulted in 89.1%, 80.2%, 83.1%, and 71.2% accuracy for Arrhythmia, Cardiomyopathy, CHD, and CAD, respectively. Likewise, the ANN algorithm resulted in 85.8%, 85.6%, 72.7%, and 69.6% accuracy for Arrhythmia, Cardiomyopathy, CHD, and CAD, respectively. Another study was proposed to predict coronary heart disease (CHD) and used the South African Heart Disease dataset of 462 samples [ 86 ]. To diagnose and improve the prediction rate of CHD, they used three supervised learning techniques, Naïve Bayes, SVM, and Decision Tree. In their study, the accuracy with library data was 83.9% for cardiovascular diseases, and for diabetes, the accuracy with library data was 95.7%. Another study on different ML techniques for predicting CHD showed that the SVM classifier achieved 95% accuracy and is superior to other ML methods [ 87 ]. Furthermore, other researchers investigated the predictive power of SVM, ANN, and Decision Tree algorithms on CHD disease for 502 samples [ 88 ]. The accuracy results of these three algorithms showed that SVM surpassed the other algorithms with an accuracy of 92.1%.

AI's massive applications in medical fields have also provided accurate prediction and detection of brain diseases. Recent ML and DL approaches are mainly used in diagnosing various brain and neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and brain tumor, which has always been very difficult to detect in the early stage [ 89 ]. AI has made it possible to process and analyze a massive amount of brain signals and data to discover insights and correlations which are not completely obvious to the human eye. The most widely used algorithm for disease detection is DL-based CNN models [ 90 , 91 ]. A recent study examined pre-trained models for predicting and detecting Alzheimer’s disease [ 92 ]. In this study, the EfficientNetB0 model outperformed other models and obtained an accuracy of 92.98%. A combination of different AI algorithms was used for the early diagnosis of Parkinson’s disease recently [ 93 ]. The best result was obtained by combining the genetic algorithm and random forest with an accuracy of 95.58%, which in turn is the best result in this field in recent works.

AI’s advanced algorithms also greatly assist in detecting and predicting breast cancer in the early stages. Breast cancer is a deadly disease among females that causes the deaths of millions of people annually [ 94 ]. However, diagnosis in the early stages has a vital role in treating and controlling. The Wisconsin Breast Cancer Dataset (WBCD) is a widely-used dataset for researchers investigating ML methods to diagnose breast cancer. The least-squares support vector machine (LSSVM) algorithm was successfully applied to WBCD to diagnose breast cancer and achieved 98.53% classification accuracy [ 95 ]. Also, a hybrid fuzzy-artificial immune system with a k-nearest neighbor algorithm was proposed on the WBCD and resulted in 99.14% classification accuracy [ 96 ]. An SVM algorithm combined with feature selection was used to diagnose breast cancer using WBCD and obtained 99.51% classification accuracy [ 97 ]. In another work, an optimized SVM was proposed to diagnose breast cancer prognosis based on the WBCD dataset [ 98 ]. This method was evaluated in two stages and obtained 96.91% and 97% accuracy, respectively. Another study presented a combination of three classifiers of SVM, K-nearest neighbors, and probabilistic neural networks to detect benign and malignant breast tumors [ 99 ]. They achieved 98.8% and 96.33% accuracy against two benchmark datasets.

Neural fuzzy methods, K-nearest neighbor, quadratic classifier, and their combination have been used to classify and diagnose breast cancer [ 100 ]. The resulting accuracy in fuzzy neural methods, KNN, quadratic classifier methods, and their combination method are 94.28%, 96.42%, 94.50%, and 97.14%, respectively. In this work, the association method provides more accurate results than every single model. A mammography-based machine learning classifier (MLC) was performed for breast cancer diagnosis to classify segmented regions on craniocaudal (CC) and/or mediolateral oblique (MLO) mammography image views [ 101 ]. They gained the area under the ROC 0.996 curve when combining features from CC and MLO views. Also, in another work, the C4.5 algorithm was used to classify the breast cancer dataset from SEER into two groups carcinomas in situ and malignant potential [ 102 ]. In the training phase, an accuracy of ~ 94% and ~ 93% in the testing phase was obtained. In a comprehensive study, several classifiers, including different classifiers decision tree (J48), Multi-Layer Perception (MLP), Naive Bayes (NB), Sequential Minimal Optimization (SMO), and Instance-Based for K-Nearest neighbor (IBK) were used to diagnose and predict breast cancer in three databases: Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC) [ 103 ]. This study used the classification accuracy and confusion matrix based on tenfold cross-validation. The integration of SMO, J48, NB, and IBK was associated with 97.2818%, 97.7153%, and 77.3196% accuracy for the WBC, WDBC, and WPBC datasets, respectively. Researchers proposed multiple data mining methods in another study to diagnose and predict breast cancer using the UCI machine learning and SEER datasets [ 104 ]. The results showed that the decision tree as the best predictor achieved 93.62% accuracy in both datasets.

ML methods, including neural networks, random forests, and support vector machines, have been used to predict and categorize genetic disorders from different amounts of genetic data. Scientists have faced challenges finding biomarkers for complicated genetic diseases due to their diverse genotypes. AI, especially ML and DL methods, could enhance the accuracy of predicting genetic disorders. For instance, the ANN-based model achieved 85.7%, 84.9%, and 84.3% for the training, testing, and validation phases, respectively [ 105 ]. The ML performance accuracy in psychiatry from genotypes varied between 48 and 95% [ 106 ]. Though genetic diseases need automated predictors based on AI, there are still some limitations in data sample size and a lack of high-standard models [ 105 ]. One of the challenges in genetic microarray analysis is identifying genes or groups of genes that are highly expressed in tumor cells but not in normal cells [ 107 ]. AI-based methods have provided a significant role in classifying cancerous microarray data. Three supervised ML techniques were proposed to organize gene data, including the C4.5 decision tree, bagged, and boosted decision trees. In this work, ensemble ML (bagged and grown decision trees) outperformed single decision trees [ 107 ]. Researchers also studied autism spectrum disorder (ASD), which has a genetic nature, using datasets of toddlers, children, adolescents, and adults to evaluate and determine the best-performing classifier of ASD. They found that MLP performed better than other classification algorithms and achieved 100% accuracy [ 108 ].

AI techniques also can have broad applications in dermatology. ML and DL can impressively be taught based on data to diagnose, predict, and classify the characteristics of various skin disease samples. However, dermatology science is still behind in accepting and using these advanced techniques. Detection in the early stages is an essential factor for effective skin cancer treatment. In this term, CNN-based algorithms can examine skin dataset images to diagnose skin cancer. Young specialists cannot always accurately detect skin cancer due to a lack of experience or human errors. So, developing automated systems based on AI can help them significantly diagnose skin diseases to save patients' lives and reduce financial costs [ 109 ]. Researchers used two ML-based strategies, ensemble learning, and DL, to analyze skin cancer lesions [ 110 ]. In this work, the DL approach outperformed the ensemble learning, for prediction demonstrated an accuracy of 91.85% and for classification of skin cancer resulted in 90.1% accuracy. The combination of Bayesian DL and an active learning approach has been used to diagnose skin cancer [ 111 ]. This approach achieved the best performance in ISIC 2016 with 75% accuracy.

The interaction of digital pathology and AI has led researchers to examine datasets more accurately and provide precise results for prostate cancer diagnoses. A vital treatment for prostate cancer is radiotherapy, but its toxicity recognition is problematic for various individuals [ 112 ]. In this case, AI could provide proper insights on predicting how a patient will react to the different therapy methods. Furthermore, AI-based technologies have demonstrated acceptable accuracy in diagnosing prostate lesions and predicting prostate cancer, patient survival rate, and treatment response. Researchers developed a supervised AI-based model for the diagnosis of prostate cancer in the early stage [ 113 ]. They used MRI images labeled with histopathology information which resulted in 89% accuracy in classification. Another study proposed a novel DL approach named XmasNet to classify prostate cancer lesions using 3D multiparametric MRI data provided by the PROSTATEx in the training phase [ 114 ]. XmasNet outperformed ML classical methods with an AUC of 0.84.

Different techniques have been proposed to detect lung cancer in the early stages; most are based on CT scan images, some utilizing x-ray images. Although it is challenging to catch it in the initial stage, it has been proven that early detection improves the survival rate of lung cancer patients [ 115 ]. Computer-assisted diagnosis (CAD) based on a DL-based framework for lung cancer diagnosis was proposed for the Kaggle Data Science Bowl 2017 challenge and placed 41 out of 1972 teams with high accuracy [ 116 ]. Deep CNN (DCNN) was used to classify different types of lung cancer into adenocarcinoma, squamous cell carcinoma, and small cell carcinoma [ 117 ]. They examined the probabilities of these three types of cancers using three-fold cross-validation and obtained 71% accuracy. This model consisted of three convolutional layers, three pooling layers, and two fully connected layers. Another research compared three deep neural networks model (CNN, DNN, and SAE) to diagnose and classify benign and malignant lung nodules using the LIDC-IDRI database [ 118 ]. CNN obtained the best performance among these three networks, with an accuracy of 84%. A comprehensive study examined three DL-based algorithms, including convolutional neural network (CNN), deep belief network (DBN), and stacked denoising autoencoder (SDAE), to diagnose lung nodules in CT images [ 119 ]. The best performance of the area under the curve (AUC) was 0.899 ± 0.018 achieved by CNN.

Diagnosis and prescribing medicines in the early stage are essential keys to treating respiratory infections. AI algorithms can assist healthcare experts in detecting and analyzing pulmonary diseases. A DL-based CNN model was presented to analyze the respiratory audio data for Chronic Obstructive Pulmonary detection and achieved 93% accuracy [ 120 ]. Also, a framework model based on CNN was proposed to diagnose Covid-19 disease using X-ray images. In this work, they achieved an accuracy of 95.7% [ 121 ].

5 Challenges, potential solutions, and future prospects of AI methods

AI has a broad role in healthcare systems for diagnosis, prediction, and prevention purposes. However, several challenges exist in using DL and ML techniques in disease diagnosis and prediction. One of the significant challenges in AI algorithms is the need for massive data in training phases which is not always practical in most diseases. Another challenge is labeling data, which requires professionalism and expertise, and is time-consuming and highly costly [ 122 ]. This can make it challenging to develop accurate models for rare or new diseases. One potential solution to the lack of labeled data is to use techniques such as data augmentation, which can be used to increase the size of the dataset artificially [ 123 ].

The complexity of computation and architecture in DL-based model is another challenge in this area. One potential solution for reducing the complexity of computation and architecture in DL models is to use model compression techniques such as pruning, quantization, and low-rank factorization [ 124 ]. These techniques can help reduce the number of parameters and computational resources required while maintaining good performance. Analysis of low-contrast images is also a challenging mission to examine patterns and features. One of the enhancement techniques used for boosting the contrast is Histogram Equalization (HE). An ML-supervised method based on hyperparameter selection using the HE technique was proposed to improve the visual appearance and increase image contrast while keeping its natural aspect [ 125 ]. Other researchers also proposed a new approach for contrast optimization based on HE in cancer diagnosis using ultrasound medical imaging [ 126 ].

Even though various researchers have recently addressed the issue of model complexity in DL [ 127 , 128 , 129 , 130 ], there is still a need for further investigation and effort in this area [ 131 ]. Through DL or ML algorithms, a constraint on the dataset is likely to create a similarly constrained model. For example, outside the pre-defined boundaries, the network may appear useless because it has not been taught how to handle such instances.

Also, more efforts are needed from medical community to convince future perspectives and acceptance of AI technologies in diagnosing and predicting various diseases. Additionally, patient privacy must be taken seriously when entering data into artificial intelligence systems [ 132 ]. Therefore, global coordination and monitoring should be done, leading to the widespread and verifiable use of AI in healthcare procedures.

In particular, ML and DL can be used to analyze large amounts of medical data, such as patient records, imaging studies, and laboratory results, in order to identify patterns that might not be obvious to human doctors [ 133 ]. This can lead to more accurate and efficient diagnosis and the ability to predict which patients are at the highest risk for certain conditions. Additionally, these techniques can be used to develop personalized treatment plans for individual patients based on their unique characteristics and medical history [ 134 , 135 ]. Also, it is vital to ensure the data used to train these models is diverse and unbiased to avoid any inaccuracies or discrimination in diagnosis and predictions [ 136 ].

In the future, ML and DL algorithms will continue to improve and become more widely adopted in the healthcare industry, leading to better disease prediction and diagnosis for patients. ML and DL techniques can be used to analyze genomic data to identify genetic markers associated with different diseases, which could lead to more precise diagnosis and personalized treatment plans. Another promising area for these techniques in healthcare is in the development of predictive models for disease progression and treatment response. These models could help physicians to identify patients at high risk for complications, or those who are unlikely to respond to certain treatments, allowing for early intervention and more effective care. As these techniques continue to advance, we can expect to see even greater improvements in patient care in the future.

6 Conclusion

DL and ML techniques have strong potential to revolutionize the field of disease diagnosis and prediction. In diagnosing the disease, the accuracy and correctness of the diagnosis is the most critical factor in the treatment process. AI has proven significant accuracy in the detection of image-based diseases as well as in the prediction of treatment outcomes regarding survival rate and treatment response. The enormous quantity of image data requires implementation into processing phases through immediate, reliable, and accurate computing power provided by AI methods. In diagnosing diseases, issues such as accuracy in detection, effective treatment, and ensuring the well-being of patients are critical. AI includes vast and diverse data, algorithms, deep computing methods, various neural networks, and emerging techniques constantly evolving to meet human needs. This study aims to investigate the performance of AI techniques in diagnosing and predicting various diseases. According to the findings of this research, SVM has the best performance for predicting heart diseases. Supervised DL networks, such as CNN-based models, are widely used due to their high accuracy and fast image recognition, especially for diagnosing in respiratory, lung, skin, and brain diseases which have led to significant results. For breast cancer diagnosis, usually combining KNN with other networks, such as SVM, leads to high accuracy in diagnosis. Therefore, DL and ML, with impressive experimental results in detecting and classifying medical images, significantly impact the success of many diseases discussed in this study. In other words, AI-based methods assist medical systems in diagnosing and predicting conditions by optimizing the use of different resources. Also, with the rapid development of AI technologies, the objective diagnosis of various diseases will no longer be an uphill task for doctors in the near future.

Data availability

Data sharing does not apply to this article as no datasets were generated or analyzed during the current study. All authors, Nafiseh Ghaffar Nia, Erkan Kaplanoglu, and Ahad Nasab have approved all the statements in this work.

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Ghaffar Nia, N., Kaplanoglu, E. & Nasab, A. Evaluation of artificial intelligence techniques in disease diagnosis and prediction. Discov Artif Intell 3 , 5 (2023). https://doi.org/10.1007/s44163-023-00049-5

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Artificial Intelligence Uncertainty Quantification in Radiotherapy Applications - A Scoping Review

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Background/purpose The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.

Methods We followed the PRISMA-ScR scoping review reporting guidelines. We utilized the population (human cancer patients), concept (utilization of AI UQ), context (radiotherapy applications) framework to structure our search and screening process. We conducted a systematic search spanning seven databases, supplemented by manual curation, up to January 2024. Our search yielded a total of 8980 articles for initial review. Manuscript screening and data extraction was performed in Covidence. Data extraction categories included general study characteristics, RT characteristics, AI characteristics, and UQ characteristics.

Results We identified 56 articles published from 2015-2024. 10 domains of RT applications were represented; most studies evaluated auto-contouring (50%), followed by image-synthesis (13%), and multiple applications simultaneously (11%). 12 disease sites were represented, with head and neck cancer being the most common disease site independent of application space (32%). Imaging data was used in 91% of studies, while only 13% incorporated RT dose information. Most studies focused on failure detection as the main application of UQ (60%), with Monte Carlo dropout being the most commonly implemented UQ method (32%) followed by ensembling (16%). 55% of studies did not share code or datasets.

Conclusion Our review revealed a lack of diversity in UQ for RT applications beyond auto-contouring. Moreover, there was a clear need to study additional UQ methods, such as conformal prediction. Our results may incentivize the development of guidelines for reporting and implementation of UQ in RT.

Competing Interest Statement

KAW serves as an Editorial Board Member for Physics and Imaging in Radiation Oncology. CDF has received travel, speaker honoraria and/or registration fee waiver unrelated to this project from: The American Association for Physicists in Medicine; the University of Alabama-Birmingham; The American Society for Clinical Oncology; The Royal Australian and New Zealand College of Radiologists; The American Society for Radiation Oncology; The Radiological Society of North America; and The European Society for Radiation Oncology.

Funding Statement

KAW was supported by an Image Guided Cancer Therapy (IGCT) T32 Training Program Fellowship from T32CA261856. ZYKs time was supported by a doctoral fellowship from the Cancer Prevention Research Institute of Texas grant #RP210042. MAN receives funding from NIH National Institute of Dental and Craniofacial Research (NIDCR) Grant (R03DE033550). CDF received/receives unrelated funding and salary support from: NIH National Institute of Dental and Craniofacial Research (NIDCR) Academic Industrial Partnership Grant (R01DE028290) and the Administrative Supplement to Support Collaborations to Improve AIML-Readiness of NIH-Supported Data (R01DE028290-04S2); NIDCR Establishing Outcome Measures for Clinical Studies of Oral and Craniofacial Diseases and Conditions award (R01DE025248); NSF/NIH Interagency Smart and Connected Health (SCH) Program (R01CA257814); NIH National Institute of Biomedical Imaging and Bioengineering (NIBIB) Research Education Programs for Residents and Clinical Fellows Grant (R25EB025787); NIH NIDCR Exploratory/Developmental Research Grant Program (R21DE031082); NIH/NCI Cancer Center Support Grant (CCSG) Pilot Research Program Award from the UT MD Anderson CCSG Radiation Oncology and Cancer Imaging Program (P30CA016672); Patient-Centered Outcomes Research Institute (PCS-1609-36195) sub-award from Princess Margaret Hospital; National Science Foundation (NSF) Division of Civil, Mechanical, and Manufacturing Innovation (CMMI) grant (NSF 1933369). CDF receives grant and infrastructure support from MD Anderson Cancer Center via: the Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program; the Program in Image-guided Cancer Therapy; and the NIH/NCI Cancer Center Support Grant (CCSG) Radiation Oncology and Cancer Imaging Program (P30CA016672). ACM received/receives funding and salary support from: NIDCR (K01DE030524, R21DE031082), the NIH National Cancer Institute (K12CA088084), and the University of Texas MD Anderson Cancer Center Charles and Daneen Stiefel Center for Head and Neck Cancer Oropharyngeal Cancer Research Program. DF was supported by R01CA195524 and NSF-2111147. Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the funders.

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I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

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Data Availability

A CSV file containing the final studies and corresponding extracted data for this scoping review are made publicly available through Figshare (doi: 10.6084/m9.figshare.25535017). All Python code used in the analysis can be found on Github (URL: https://github.com/kwahid/RT_UQ_scoping_review/tree/main). Data will be private until formal manuscript acceptance in journal.

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