artificial intelligence for literature review

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Title: artificial intelligence for literature reviews: opportunities and challenges.

Abstract: This manuscript presents a comprehensive review of the use of Artificial Intelligence (AI) in Systematic Literature Reviews (SLRs). A SLR is a rigorous and organised methodology that assesses and integrates previous research on a given topic. Numerous tools have been developed to assist and partially automate the SLR process. The increasing role of AI in this field shows great potential in providing more effective support for researchers, moving towards the semi-automatic creation of literature reviews. Our study focuses on how AI techniques are applied in the semi-automation of SLRs, specifically in the screening and extraction phases. We examine 21 leading SLR tools using a framework that combines 23 traditional features with 11 AI features. We also analyse 11 recent tools that leverage large language models for searching the literature and assisting academic writing. Finally, the paper discusses current trends in the field, outlines key research challenges, and suggests directions for future research.

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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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Methodology

• Open access
• Published: 28 April 2022

An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain

• Renu Sabharwal   ORCID: orcid.org/0000-0001-9728-8001 1 &
• Shah J. Miah 1  

Journal of Big Data volume  9 , Article number:  53 ( 2022 ) Cite this article

6508 Accesses

8 Citations

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Big data analytics utilizes different techniques to transform large volumes of big datasets. The analytics techniques utilize various computational methods such as Machine Learning (ML) for converting raw data into valuable insights. The ML assists individuals in performing work activities intelligently, which empowers decision-makers. Since academics and industry practitioners have growing interests in ML, various existing review studies have explored different applications of ML for enhancing knowledge about specific problem domains. However, in most of the cases existing studies suffer from the limitations of employing a holistic, automated approach. While several researchers developed various techniques to automate the systematic literature review process, they also seemed to lack transparency and guidance for future researchers. This research aims to promote the utilization of intelligent literature reviews for researchers by introducing a step-by-step automated framework. We offer an intelligent literature review to obtain in-depth analytical insight of ML applications in the clinical domain to (a) develop the intelligent literature framework using traditional literature and Latent Dirichlet Allocation (LDA) topic modeling, (b) analyze research documents using traditional systematic literature review revealing ML applications, and (c) identify topics from documents using LDA topic modeling. We used a PRISMA framework for the review to harness samples sourced from four major databases (e.g., IEEE, PubMed, Scopus, and Google Scholar) published between 2016 and 2021 (September). The framework comprises two stages—(a) traditional systematic literature review consisting of three stages (planning, conducting, and reporting) and (b) LDA topic modeling that consists of three steps (pre-processing, topic modeling, and post-processing). The intelligent literature review framework transparently and reliably reviewed 305 sample documents.

Introduction

Organizations are continuously harnessing the power of various big data adopting different ML techniques. Captured insights from big data may create a greater impact to reshape their business operations and processes. As a vital technique, big data analytics methods are used to transform complicated and huge amounts of data, known as ‘Big Data, in order to uncover hidden patterns, new learning, untold facts or associations, anomalies, and other perceptions [ 41 ]. Big Data alludes to the enormous amount of data that a traditional database management system cannot handle. In most of the cases, traditional software functions would be inadequate to analyze or process them. Big data are characterized by the 5 V’s, which refers to volume, variety, velocity, veracity, and value [ 22 ]. ML is a vital approach to design useful big data analytics techniques, which is a rapidly growing sub-field in information sciences that deals with all these characteristics. ML employs numerous methods for machines to learn from past experiences (e.g., past datasets) reducing the extra burden of writing codes in traditional programming [ 7 , 26 ]. Clinical care enterprises face a huge challenge due to the increasing demand of big data processing to improve clinical care outcomes. For example, an electronic health record contains a huge amount of patient information, drug administration, imaging data using various modalities. The variety and quantity of the huge data provide in the clinical domain as an ideal topic to appraise the value of ML in research.

Existing ML approaches, such as Oala et al. [ 35 ] proposed an algorithmic framework that give a path towards the effective and reliable application of ML in the healthcare domain. In conjunction with their systematic review, our research offers a smart literature review that consolidates a traditional literature review followed the PRISMA framework guidelines and topic modeling using LDA, focusing on the clinical domain. Most of the existing literature focused on the healthcare domain [ 14 , 42 , 49 ] are more inclusive and of a broader scope with a requisite of medical activities, whereas our research is primarily focused is clinical, which assist in diagnosing and treating patients as well as includes clinical aspects of medicine.

Since clinical research has developed, the area has become increasingly attractive to clinical researchers, in particular for learning insights of ML applications in clinical practices . This is because of its practical pertinence to clinical patients, professionals, clinical application designers, and other specialists supported by the omnipresence of clinical disease management techniques. Although the advantage is presumed for the target audience, such as self-management abilities (self-efficacy and investment behavior) and physical or mental condition of life amid long-term ill patients, clinical care specialists (such as further developing independent direction and providing care support to patients), their clinical care have not been previously assessed and conceptualized as a well-defined and essential sub-field of health care research. It is important to portray similar studies utilizing different types of review approaches in the aspect of the utilization of ML/DL and its value. Table 1 represents some examples of existing studies with various points and review approaches in the domain.

Although the existing studies included in Table 1 give an understanding of designated aspects of ML/DL utilization in clinical care, they show a lack of focus on how key points addressed in existing ML/DL research are developing. Further to this, they indicate a clear need towards an understanding of multidisciplinary affiliations and profiles of ML/DL that could provide significant knowledge to new specialists or professionals in this space. For instance, Brnabic and Hess [ 8 ] recommended a direction for future research by stating that “ Future work should routinely employ ensemble methods incorporating various applications of machine learning algorithms” (p. 1).

ML tools have become the central focus of modern biomedical research, because of better admittance to large datasets, exponential processing power, and key algorithmic developments allowing ML models to handle increasingly challenging data [ 19 ]. Different ML approaches can analyze a huge amount of data, including difficult and abnormal patterns. Most studies have focused on ML and its impacts on clinical practices [ 2 , 9 , 10 , 24 , 26 , 34 , 43 ]. Fewer studies have examined the utilization of ML algorithms [ 11 , 20 , 45 , 48 ] for more holistic benefits for clinical researchers.

ML becomes an interdisciplinary science that integrates computer science, mathematics, and statistics. It is also a methodology that builds smart machines for artificial intelligence. Its applications comprise algorithms, an assortment of instructions to perform specific tasks, crafted to independently learn from data without human intercession. Over time, ML algorithms improve their prediction accuracy without a need for programming. Based on this, we offer an intelligent literature review using traditional literature review and Latent Dirichlet Allocation (LDA Footnote 1 ) topic modeling in order to meet knowledge demands in the clinical domain. Theoretical measures direct the current study results because previous literature provides a strong foundation for future IS researchers to investigate ML in the clinical sector. The main aim of this study is to develop an intelligent literature framework using traditional literature. For this purpose, we employed four digital databases -IEEE, Google Scholar, PubMed, and Scopus then performed LDA topic modeling, which may assist healthcare or clinical researchers in analyzing many documents intelligently with little effort and a small amount of time.

Traditional systematic literature is destined to be obsolete, time-consuming with restricted processing power, resulting in fewer sample documents investigated. Academic and practitioner-researchers are frequently required to discover, organize, and comprehend new and unexplored research areas. As a part of a traditional literature review that involves an enormous number of papers, the choice for a researcher is either to restrict the number of documents to review a priori or analyze the study using some other methods.

The proposed intelligent literature review approach consists of Part A and Part B, a combination of traditional systematic literature review and topic modeling that may assist future researchers in using appropriate technology, producing accurate results, and saving time. We present the framework below in Fig.  1 .

Proposed intelligent literature review framework

The traditional literature review identified 534,327 articles embraces Scopus (24,498), IEEE (2558), PubMed (11,271), and Google Scholar (496,000) articles, which went through three stages–Planning the review, conducting the review, and reporting the review and analyzed 305 articles, where we performed topic modeling using LDA.

We follow traditional systematic literature review methodologies [ 25 , 39 , 40 ] including a PRISMA framework [ 37 ]. We review four digital databases and deliberately develop three stages entailing planning, conducting, and reporting the review (Fig.  2 ).

Traditional literature review three stages

Planning the review

Research articles : the research articles are classified using some keywords mentioned below in Tables 2 , 3 .

Digital database : Four databases (IEEE, PubMed, Scopus, and Google Scholar) were used to collect details for reviewing research articles.

Review protocol development : We first used Scopus to search the information and found many studies regarding this review. We then searched PubMed, IEEE, and Google scholar for articles and extracted only relevant papers matching our keywords and review context based on their full-text availability.

Review protocol evaluation : To support the selection of research articles and inclusion and exclusion criteria, the quality of articles was explored and assessed to appraise their suitability and impartiality [ 44 ]. Only articles with keywords “machine learning” and “clinical” in document titles and abstracts were selected.

Conducting the review

The second step is conducting the review, which includes a description of Search Syntax and data synthesis.

Search syntax Table 4 details the syntax used to select research articles.

Data synthesis

We used a qualitative meta-synthesis technique to understand the methodology, algorithms, applications, qualities, results, and current research impediments. Qualitative meta-synthesis is a coherent approach for analyzing data across qualitative studies [ 4 ]. Our first search identified 534,327 papers, comprising Scopus (24,498), IEEE (2,558), PubMed (11,271), and Google Scholar (496,000) articles with the selected keywords. After subjecting this dataset to our inclusion and exclusion criteria, articles were reduced to Scopus (181), IEEE (62), PubMed (37), and Google Scholar (46) (Fig.  3 ).

PRISMA framework of traditional literature review

Reporting the review

This section displays the result of the traditional literature review.

Demonstration of findings

A search including linear literature and citation chaining was acted in digital databases, and the resulted papers were thoroughly analyzed to choose only the most pertinent articles, at last, 305 articles were included for the Part B review. Information of such articles were classified, organized, and demonstrated to show the finding.

Report the findings

The word cloud is displayed on the selected 305 research articles which give an overview of the frequency of the word within those 305 research articles. The chosen articles are moved to the next step to perform the conversion of PDF files to text documents for performing LDA topic modeling (Fig. 4 ).

Word cloud on 305 articles

Conversion of pdf files to a text document

The Python coding is used to convert pdf files shared on GitHub https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git . The one text document is prepared with 305 research papers collected from a traditional literature review.

Topic modelling for intelligent literature review

Our intelligent literature review is developed using a combination of traditional literature review and topic modeling [ 22 ]. We use topic modeling—probability generating, a text-mining technique widely used in computer science for text mining and data recovery. Topic modeling is used in numerous papers to analyze [ 1 , 5 , 17 , 36 ] and use various ML algorithms [ 38 ] such as Latent Semantic Indexing (LSI), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), Parallel Latent Dirichlet Allocation (PLDA), and Pachinko Allocation Model (PAM). We developed the LDA-based methodological framework so it would be most widely and easily used [ 13 , 17 , 21 ] as a very elementary [ 6 ] approach. LDA is an unsupervised and probabilistic ML algorithm that discovers topics by calculating patterns of word co-occurrence across many documents or corpus [ 16 ]. Each LDA topic is distributed across each document as a probability.

While there are numerous ways of conducting a systematic literature review, most strategies require a high expense of time and prior knowledge of the area in advance. This study examined the expense of various text categorization strategies, where the assumptions and cost of the strategy are analyzed [ 5 ]. Interestingly, except manually reading the articles and topic modeling, all the strategies require prior knowledge of the articles' categories and high pre-examination costs. However, topic modeling can be automated, alternate the utilization of researchers' time, demonstrating a perfect match for the utilization of topic modeling as a part of an Intelligent literature review. Topic modeling has been used in a few papers to categorize research papers presented in Table 5 .

The articles/papers in the above table analyzed are speeches, web documents, web posts, press releases, and newspapers. However, none of those have developed the framework to perform traditional literature reviews from digital databases then use topic modeling to save time. However, this research points out the utilization of LDA in academics and explores four parameters—text pre-processing, model parameters selection, reliability, and validity [ 5 ]. Topic modeling identifies patterns of the repetitive word across a corpus of documents. Patterns of word co-occurrence are conceived as hidden ‘topics’ available in the corpus. First, documents must be modified to be machine-readable, with only their most informative features used for topic modeling. We modify documents in a three-stage process entailing pre-processing, topic modeling, and post-processing, as defined in Fig.  1 earlier.

The utilization of topic modeling presents an opportunity for researchers to use advanced technology for the literature review process. Topic modeling has been utilized online and requires many statistical skills, which not all researchers have. Therefore, we have shared the codes in GitHub with the default parameter for future researchers.

Pre-processing

Székely and Brocke [ 46 ] explained that pre-processing is a seven-step process which explored below and mentioned in Fig.  1 as part B:

Load data—The text data file is imported using the python command.

Optical character recognition—using word cloud, characters are recognized.

Filtering non-English words—non-English words are removed.

Document tokenization—Split the text into sentences and the sentences into words. Lowercase the words and remove punctuation.

Text cleaning—the text has been cleaned using portstemmer.

Word lemmatization—words in the third person are changed to the first person, and past and future verb tenses are changed into the present.

Stop word removal—All stop words are removed.

Topic modelling using LDA

Several research articles have been selected to run LDA topic modeling, explained in Table 5 . LDA model results present the coherence score for all the selected topics and a list of the most frequently used words for each.

Post-processing

The goal of the post-processing stage is to identify and label topics and topics relevant for use in the literature review. The result of the LDA model is presented as a list of topics and probabilities of each document (paper). The list is utilized to assign a paper to a topic by arranging the list by the highest probability for each paper for each topic. All the topics contain documents that are like each other. To reduce the risk of error in topic identification, a combination of inspecting the most frequent words for each topic and a paper view is used. After the topic review, it will present in the literature review.

Following the intelligent literature review, results of the LDA model should be approved or validated by statistical, semantic, or predictive means. Statistical validation defines the mutual information tests of result fit to model assumptions; semantics validation requires hand-coding to decide if the importance of specific words varies significantly and as expected with tasks to different topics which is used in the current study to validate LDA model result; and predictive validation refers to checking if events that ought to have expanded the prevalence of particular topic if out interpretations are right, did so [ 6 , 21 ].

LDA defines that each word in each document comes from a topic, and the topic is selected from a set of keywords. So we have two matrices:

ϴtd = P(t|d) which is the probability distribution of topics in documents

Фwt = P(w|t), which is the probability distribution of words in topics

And, we can say that the probability of a word given document, i.e., P(w|d), is equal to:

where T is the total number of topics; likewise, let’s assume there are W keywords for all the documents.

If we assume conditional independence, we can say that

And hence P(w|d) is equal to

that is the dot product of ϴtd and Фwt for each topic t.

Our systematic literature review identified 305 research papers after performing a traditional literature review. After executing LDA topic modeling, only 115 articles show the relevancy with our topic "machine learning application in clinical domain'. The following stages present LDA topic modeling process.

The 305 research papers were stacked into a Python environment then converted into a single text file. The seven steps have been carried out, described earlier in Pre-processing .

• Topic modeling

The two main parameters of the LDA topic model are the dictionary (id2word)-dictionary and the corpus—doc_term_matrix. The LDA model is created by running the command:

# Creating the object for LDA model using gensim library

LDA = gensim.models.ldamodel.LdaModel

# Build LDA model

lda_model = LDA(corpus=doc_term_matrix, id2word = dictionary, num_topics=20, random_state=100,

chunksize = 1000, passes=50,iterations=100)

In this model, ‘num_topics’ = 20, ‘chunksize’ is the number of documents used in each training chunk, and ‘passes’ is the total number of training passes.

Firstly, the LDA model is built with 20 topics; each topic is represented by a combination of 20 keywords, with each keyword contributing a certain weight to a topic. Topics are viewed and interpreted in the LDA model, such as Topic 0, represented as below:

(0, '0.005*"analysis" + 0.005*"study" + 0.005*"models" + 0.004*"prediction" + 0.003*"disease" + 0.003*"performance" + 0.003*"different" + 0.003*"results" + 0.003*"patient" + 0.002*"feature" + 0.002*"system" + 0.002*"accuracy" + 0.002*"diagnosis" + 0.002*"classification" + 0.002*"studies" + 0.002*"medicine" + 0.002*"value" + 0.002*"approach" + 0.002*"variables" + 0.002*"review"'),

Our approach to finding the ideal number of topics is to construct LDA models with different numbers of topics as K and select the model with the highest coherence value. Selecting the ‘K' value that denotes the end of the rapid growth of topic coherence ordinarily offers significant and interpretable topics. Picking a considerably higher value can provide more granular sub-topics if the ‘K’ selection is too large, which can cause the repetition of keywords in multiple topics.

Model perplexity and topic coherence values are − 8.855378536321144 and 0.3724024189689453, respectively. To measure the efficiency of the LDA model is lower the perplexity, the better the model is. Topics and associated keywords were then examined in an interactive chart using the pyLDAvis package, which presents the topics are 20 and most salient terms in those 20 topics, but these 20 topics overlap each other as shown in Fig.  5 , which means the keywords are repeated in these 20 topics and topics are overlapped, which means so decided to use num_topics = 9 and presented PyLDAvis Figure below. Each bubble on the left-hand side plot represents a topic. The bigger the bubble is, the more predominant that topic is. A decent topic will have a genuinely big, non-overlapping bubble dispersed throughout the graph instead of grouped in one quadrant. A topic model with many topics will typically have many overlaps, small-sized bubbles clustered in one locale of the graph, as shown in Fig.  6 .

PyLDAvis graph with 20 topics in the clinical domain

PyLDAvis graph with nine vital topics in the clinical domain

Each bubble addresses a generated topic. The larger the bubble, the higher percentage of the number of keywords in the corpus is about that topic which can be seen on the GitHub file. Blue bars address the general occurrence of each word in the corpus. If no topic is selected, the blue bars of the most frequently used words are displayed, as depicted in Fig.  6 .

The further the bubbles are away from each other, the more various they are. For example, we can tell that topic 1 is about patient information and studies utilized deep learning to analyze the disease, which can be seen in GitHub file codes ( https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git ) and presented in Fig.  7 .

PyLDAvis graph with topic 1

Red bars give the assessed number of times a given topic produced a given term. As you can see from Fig.  7 , there are around 4000 of the word 'analysis', and this term is utilized 1000 times inside topic 1. The word with the longest red bar is the most used by the keywords having a place with that topic.

A good topic model will have big and non-overlapping bubbles dispersed throughout the chart. As we can see from Fig.  6 , the bubbles are clustered within one place. One of the practical applications of topic modeling is discovering the topic in a provided document. We find out the topic number with the highest percentage contribution in that document, as shown in Fig.  8 .

Dominant topics with topic percentage contribution

The next stage is to process the discoveries and find a satisfactory depiction of the topics. A combination of evaluating the most continuous words utilized to distinguish the topic. For example, the most frequent words for the papers in topic 2 are "study" and "analysis", which indicate frequent words for ML usage in the clinical domain.

The topic name is displayed with the topic number from 0 to 8, which represents in the Table 6 , which includes the Topic number and Topic words.

The result represents the percentage of the topics in all documents, which presents that topic 0 and topic 6 have the highest percentage and used in 58 and 57 documents, respectively, with 115 papers. The result of this research was an overview of the exploration areas inside the paper corpus, addressed by 9 topics.

This paper presented a new methodology that is uncommon in scholarly publications. The methodology utilizes ML to investigate sample articles/papers to distinguish research directions. Even though the structure of the ML-based methodology has its restrictions, the outcomes and its ease of use leave a promising future for topic modeling-based systematic literature reviews.

The principal benefit of the methodological framework is that it gives information about an enormous number of papers, with little effort on the researcher's part, before time-exorbitant manual work is to be finished. By utilizing the framework, it is conceivable to rapidly explore a wide range of paper corpora and assess where the researcher's time and concentration should be spent. This is particularly significant for a junior researcher with minimal earlier information on a research field. If default boundaries and cleaning settings can be found for the steps in the framework, a completely programmed gathering of papers could be empowered, where limited works have been introduced to accomplish an overview of research directions.

From a literature review viewpoint, the advantage of utilizing the proposed framework is that the inclusion and exclusion selection of papers for a literature review will be delayed to a later stage where more information is given, resulting in a more educated dynamic interaction. The framework empowers reproducibility, as every step can be reproduced in the systematic review process that ultimately empowers with transparency. The whole process has been demonstrated as a case concept on GitHub by future researchers.

The study has introduced an intelligent literature review framework that uses ML to analyze existing research documents or articles. We demonstrate how topic modeling can assist literature review by reducing the manual screening of huge quantities of literature for more efficient use of researcher time. An LDA algorithm provides default parameters and data cleaning steps, reducing the effort required to review literature. An additional advantage of our framework is that the intelligent literature review offers accurate results with little time, and it comprises traditional ways to analyze literature and LDA topic modeling.

This framework is constructed in a step-by-step manner. Researchers can use it efficiently because it requires less technical knowledge than other ML algorithms. There is no restriction on the quantity of the research papers it can measure. This research extends knowledge to similar studies in this field [ 12 , 22 , 23 , 26 , 30 , 46 ] which present topic modeling. The study acknowledges the inspiring concept of smart literature defined by Asmussen and Møller [ 3 ]. The researchers previously provided a brief description of how LDA is utilized in topic modeling. Our research followed the basic idea but enhanced its significance to broaden its scale and focus on a specific domain such as the clinical domain to produce insights from existing research articles. For instance, Székely and Vom [ 46 ] utilized natural language processing to analyze 9514 sustainability reports published between 1999 and 2015. They identified 42 topics but did not develop any framework for future researchers. This was considered a significant gap in the research. Similarly, Kushwaha et al. [ 22 ] used a network analysis approach to analyze 10-year papers without providing any clear transparent outcome (e.g., how the research step-by-step produces an outcome). Likewise, Asmussen and Møller [ 3 ] developed a smart literature review framework that was limited to analyzing 650 sample articles through a single method. However, in our research, we developed an intelligent literature review that combines traditional and LDA topic modeling, so that future researchers can get assistance to gain effective knowledge regarding literature review when it becomes a state-of-the-art in research domains.

Our research developed a more effective intelligent framework, which combines traditional literature review and topic modeling using LDA, which provides more accurate and transparent results. The results are shared via public access on GitHub using this link https://github.com/MachineLearning-UON/Topic-modeling-using-LDA.git .

This paper focused on creating a methodological framework to empower researchers, diminishing the requirement for manually scanning documents and assigning the possibility to examine practically limitless. It would assist in capturing insights of an enormous number of papers quicker, more transparently, with more reliability. The proposed framework utilizes the LDA's topic model, which gathers related documents into topics.

A framework employed topic modeling for rapidly and reliably investigating a limitless number of papers, reducing their need to read individually, is developed. Topic modeling using the LDA algorithm can assist future researchers as they often need an outline of various research fields with minimal pre-existing knowledge. The proposed framework can empower researchers to review more papers in less time with more accuracy. Our intelligent literature review framework includes a holistic literature review process (conducting, planning, and reporting the review) and an LDA topic modeling (pre-processing, topic modeling, and post-processing stages), which conclude the results of 115 research articles are relevant to the search.

The automation of topic modeling with default parameters could also be explored to benefit non-technical researchers to explore topics or related keywords in any problem domain. For future directions, the principal points should be addressed. Future researchers in other research fields should apply the proposed framework to acquire information about the practical usage and gain ideas for additional advancement of the framework. Furthermore, research in how to consequently specify model parameters could extraordinarily enhance the ease of use for the utilization of topic modeling for non-specialized researchers, as the determination of model parameters enormously affects the outcome of the framework.

Future research may be utilized more ML analytics tools as complete solution artifacts to analyze different forms of big data. This could be adopting design science research methodologies for benefiting design researchers who are interested in building ML-based artifacts [ 15 , 28 , 29 , 31 , 32 , 33 ].

Availability of data and materials

Data will be supplied upon request.

LDA is a probabilistic method for topic modeling in text analysis, providing both a predictive and latent topic representation.

Abbreviations

The Institute of Electrical and Electronics Engineers

• Machine learning
• Latent Dirichlet Allocation

Organizational Capacity

Latent Semantic Indexing

Latent Semantic Analysis

Non-Negative Matrix Factorization

Parallel Latent Dirichlet Allocation

Pachinko Allocation Model

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Sabharwal, R., Miah, S.J. An intelligent literature review: adopting inductive approach to define machine learning applications in the clinical domain. J Big Data 9 , 53 (2022). https://doi.org/10.1186/s40537-022-00605-3

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The potential for artificial intelligence to transform healthcare: perspectives from international health leaders

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Artificial intelligence (AI) has the potential to transform care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care. AI will be critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. There is also universal concern about the ability to monitor health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change. The Future of Health (FOH), an international community of senior health care leaders, collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise around this topic. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers across the globe that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

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Introduction

Artificial intelligence (AI), supported by timely and accurate data and evidence, has the potential to transform health care delivery by improving health outcomes, patient safety, and the affordability and accessibility of high-quality care 1 , 2 . AI integration is critical to building an infrastructure capable of caring for an increasingly aging population, utilizing an ever-increasing knowledge of disease and options for precision treatments, and combatting workforce shortages and burnout of medical professionals. However, we are not currently on track to create this future. This is in part because the health data needed to train, test, use, and surveil these tools are generally neither standardized nor accessible. This is true across the international community, although there is variable progress within individual countries. There is also universal concern about monitoring health AI tools for changes in performance as they are implemented in new places, used with diverse populations, and over time as health data may change.

The Future of Health (FOH) is an international community of senior health care leaders representing health systems, health policy, health care technology, venture funding, insurance, and risk management. FOH collaborated with the Duke-Margolis Institute for Health Policy to conduct a literature review, expert convening, and consensus-building exercise. In total, 46 senior health care leaders were engaged in this work, from eleven countries in Europe, North America, Africa, Asia, and Australia. This commentary summarizes the four priority action areas and recommendations for health care organizations and policymakers that FOH members identified as important for fully realizing AI’s potential in health care: improving data quality to power AI, building infrastructure to encourage efficient and trustworthy development and evaluations, sharing data for better AI, and providing incentives to accelerate the progress and impact of AI.

Powering AI through high-quality data

“Going forward, data are going to be the most valuable commodity in health care. Organizations need robust plans about how to mobilize and use their data.”

AI algorithms will only perform as well as the accuracy and completeness of key underlying data, and data quality is dependent on actions and workflows that encourage trust.

To begin to improve data quality, FOH members agreed that an initial priority is identifying and assuring reliable availability of high-priority data elements for promising AI applications: those with the most predictive value, those of the highest value to patients, and those most important for analyses of performance, including subgroup analyses to detect bias.

Leaders should also advocate for aligned policy incentives to improve the availability and reliability of these priority data elements. There are several examples of efforts across the world to identify and standardize high-priority data elements for AI applications and beyond, such as the multinational project STANDING Together, which is developing standards to improve the quality and representativeness of data used to build and test AI tools 3 .

Policy incentives that would further encourage high-quality data collection include (1) aligned payment incentives for measures of health care quality and safety, and ensuring the reliability of the underlying data, and (2) quality measures and performance standards focused on the reliability, completeness, and timeliness of collection and sharing of high-priority data itself.

Trust and verify

“Your AI algorithms are only going to be as good as the data and the real-world evidence used to validate them, and the data are only going to be as good as the trust and privacy and supporting policies.”

FOH members stressed the importance of showing that AI tools are both effective and safe within their specific patient populations.

This is a particular challenge with AI tools, whose performance can differ dramatically across sites and over time, as health data patterns and population characteristics vary. For example, several studies of the Epic Sepsis Model found both location-based differences in performance and degradation in performance over time due to data drift 4 , 5 . However, real-world evaluations are often much more difficult for algorithms that are used for longer-term predictions, or to avert long-term complications from occurring, particularly in the absence of connected, longitudinal data infrastructure. As such, health systems must prioritize implementing data standards and data infrastructure that can facilitate the retraining or tuning of algorithms, test for local performance and bias, and ensure scalability across the organization and longer-term applications 6 .

There are efforts to help leaders and health systems develop consensus-based evaluation techniques and infrastructure for AI tools, including HealthAI: The Global Agency for Responsible AI in Health, which aims to build and certify validation mechanisms for nations and regions to adopt; and the Coalition for Health AI (CHAI), which recently announced plans to build a US-wide health AI assurance labs network 7 , 8 . These efforts, if successful, will assist manufacturers and health systems in complying with new laws, rules, and regulations being proposed and released that seek to ensure AI tools are trustworthy, such as the EU AI Act and the 2023 US Executive Order on AI.

Sharing data for better AI

“Underlying these challenges is the investment required to standardize business processes so that you actually get data that’s usable between institutions and even within an institution.”

While high-quality internal data may enable some types of AI-tool development and testing, this is insufficient to power and evaluate all AI applications. To build truly effective AI-enabled predictive software for clinical care and predictive supports, data often need to be interoperable across health systems to build a diverse picture of patients’ health across geographies, and reliably shared.

FOH members recommended that health care leaders work with researchers and policymakers to connect detailed encounter data with longitudinal outcomes, and pilot opportunities across diverse populations and systems to help assure valid outcome evaluations as well as address potential confounding and population subgroup differences—the ability to aggregate data is a clear rate-limiting step. The South African National Digital Health Strategy outlined interventions to improve the adoption of digital technologies while complying with the 2013 Protection of Personal Information Act 9 . Although challenges remain, the country has made progress on multiple fronts, including building out a Health Patient Registration System as a first step towards a portable, longitudinal patient record system and releasing a Health Normative Standards Framework to improve data flow across institutional and geographic boundaries 10 .

Leaders should adopt policies in their organizations, and encourage adoption in their province and country, that simplify data governance and sharing while providing appropriate privacy protections – including building foundations of trust with patients and the public as previously discussed. Privacy-preserving innovations include ways to “share” data without movement from protected systems using approaches like federated analyses, data sandboxes, or synthetic data. In addition to exploring privacy-preserving approaches to data sharing, countries and health systems may need to consider broad and dynamic approaches to consent 11 , 12 . As we look to a future where a patient may have thousands of algorithms churning away at their data, efforts to improve data quality and sharing should include enabling patients’ access to and engagement with their own data to encourage them to actively partner in their health and provide transparency on how their data are being used to improve health care. For example, the Understanding Patient Data program in the United Kingdom produces research and resources to explain how the National Health Service uses patients’ data 13 . Community engagement efforts can further assist with these efforts by building trust and expanding understanding.

FOH members also stressed the importance of timely data access. Health systems should work together to establish re-usable governance and privacy frameworks that allow stakeholders to clearly understand what data will be shared and how it will be protected to reduce the time needed for data use agreements. Trusted third-party data coordinating centers could also be used to set up “precertification” systems around data quality, testing, and cybersecurity to support health organizations with appropriate data stewardship to form partnerships and access data rapidly.

Incentivizing progress for AI impact

“Unless it’s tied to some kind of compensation to the organization, the drive to help implement those tools and overcome that risk aversion is going to be very high… I do think that business driver needs to be there.”

AI tools and data quality initiatives have not moved as quickly in health care due to the lack of direct payment, and often, misalignment of financial incentives and supports for high-quality data collection and predictive analytics. This affects both the ability to purchase and safely implement commercial AI products as well as the development of “homegrown” AI tools.

FOH members recommended that leaders should advocate for paying for value in health – quality, safety, better health, and lower costs for patients. This better aligns the financial incentives for accelerating the development, evaluation, and adoption of AI as well as other tools designed to either keep patients healthy or quickly diagnose and treat them with the most effective therapies when they do become ill. Effective personalized health care requires high-quality, standardized, interoperable datasets from diverse sources 14 . Within value-based payments themselves, data are critical to measuring quality of care and patient outcomes, adjusted or contextualized for factors outside of clinical control. Value-based payments therefore align incentives for (1) high-quality data collection and trusted use, (2) building effective AI tools, and (3) ensuring that those tools are improving patient outcomes and/or health system operations.

Data have become the most valuable commodity in health care, but questions remain about whether there will be an AI “revolution” or “evolution” in health care delivery. Early AI applications in certain clinical areas have been promising, but more advanced AI tools will require higher quality, real-world data that is interoperable and secure. The steps health care organization leaders and policymakers take in the coming years, starting with short-term opportunities to develop meaningful AI applications that achieve measurable improvements in outcomes and costs, will be critical in enabling this future that can improve health outcomes, safety, affordability, and equity.

Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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Acknowledgements

The authors acknowledge Oranit Ido and Jonathan Gonzalez-Smith for their contributions to this work. This study was funded by The Future of Health, LLC. The Future of Health, LLC, was involved in all stages of this research, including study design, data collection, analysis and interpretation of data, and the preparation of this manuscript.

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C.S., K.H., N.R., and R.S. conducted initial background research and analyzed qualitative data from stakeholders. All authors (C.S., E.Z., K.H., N.R., R.S., M.M., C.K., C.A.S., and D.B.) assisted with conceptualization of the project and strategic guidance. C.S., K.H., and N.R. wrote initial drafts of the manuscript. All authors contributed to critical revisions of the manuscript and read and approved the final manuscript.

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C.S., K.H., N.R., and C.A.S. declare no competing interests. E.Z. reports personal fees from Arkin Holdings, personal fees from Statista and equity from Valera Health, Profility and Hello Heart. R.S. has been an external reviewer for The John A. Hartford Foundation, and is a co-chair for the Health Evolution Summit Roundtable on Value-Based Care for Specialized Populations. M.M. is an independent director on the boards of Johnson & Johnson, Cigna, Alignment Healthcare, and PrognomIQ; co-chairs the Guiding Committee for the Health Care Payment Learning and Action Network; and reports fees for serving as an adviser for Arsenal Capital Partners, Blackstone Life Sciences, and MITRE. C.K. is a Profility Board member and additionally reports equity from Valera Health and MDClone. D.W.B. reports grants and personal fees from EarlySense, personal fees from CDI Negev, equity from Valera Health, equity from Clew, equity from MDClone, personal fees and equity from AESOP, personal fees and equity from Feelbetter, equity from Guided Clinical Solutions, and grants from IBM Watson Health, outside the submitted work. D.W.B. has a patent pending (PHC-028564 US PCT), on intraoperative clinical decision support.

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Silcox, C., Zimlichmann, E., Huber, K. et al. The potential for artificial intelligence to transform healthcare: perspectives from international health leaders. npj Digit. Med. 7 , 88 (2024). https://doi.org/10.1038/s41746-024-01097-6

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Understanding Artificial Intelligence in Research Libraries – Extensive Literature Review

Artificial intelligence (AI) now forms a part of various activities in the academic world. AI will also affect how research libraries perform and carry out their services and how the various kinds of data they hold in their repositories will be used in the future. For the moment, the landscape is complex and unclear, and library personnel and leaders are uncertain about where they should lay the path ahead. This extensive literature review provides an overview of how research libraries understand, react to, and work with AI. This paper examines the roles conceived for libraries and librarians, their users, and AI. Finally, design thinking is presented as an approach to solving emerging issues with AI and opening up opportunities for this technology at a more strategic level.

Author Biographies

Andrea gasparini, university of oslo library.

Gasparini holds a Ph.D. from the Department of Informatics, University of Oslo, Norway. He worked for 20 years as a chief engineer at the University of Oslo Library, and last year as a lecturer in Design at the Department of Informatics, University of Oslo, Norway. He has been working with e-readers and tablet PCs in a primary school and the University of Oslo for his master thesis. His Ph.D. is about the use of Design Thinking and Service Design in academic libraries. Andrea Gasparini had the possibility to test his approach in Norway, Italy, USA, and Uganda. In addition, he has received in 2017 a grant to study ways of using Artificial Intelligence (AI) combined with a designerly and user-centred approach in the context of an academic library. His publications include book chapters, journal papers, and conference papers, and conference papers in the field of Design Thinking in diverse contexts as well as the use of technologies in schools.

Heli Kautonen, Finnish Literature Society Library

Heli Kautonen, PhD, works as Director of the Finnish Literature Society Library in Helsinki, Finland. Earlier, she has worked for digital cultural heritage in different positions for over 15 years. She was one of the managers in the team at the National Library of Finland, which built the Finna service that joins together the collections of practically all Finnish archives, libraries and museums. She has been involved in various activities in her work as a university teacher, communications team member and work package leader in a European Union project. Heli Kautonen’s formal education ranges from art and design to information technology. Her doctoral thesis from the Aalto University School of Science studied the strategic aspects of user-centred design (UCD) in public digital services. Her current research interests are in applying human-centred design viewpoints to solving the societal challenges in the age of digitalisation and, particularly, artificial intelligence in the context of research libraries.

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Publics’ views on ethical challenges of artificial intelligence: a scoping review

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This scoping review examines the research landscape about publics’ views on the ethical challenges of AI. To elucidate how the concerns voiced by the publics are translated within the research domain, this study scrutinizes 64 publications sourced from PubMed ® and Web of Science™. The central inquiry revolves around discerning the motivations, stakeholders, and ethical quandaries that emerge in research on this topic. The analysis reveals that innovation and legitimation stand out as the primary impetuses for engaging the public in deliberations concerning the ethical dilemmas associated with AI technologies. Supplementary motives are rooted in educational endeavors, democratization initiatives, and inspirational pursuits, whereas politicization emerges as a comparatively infrequent incentive. The study participants predominantly comprise the general public and professional groups, followed by AI system developers, industry and business managers, students, scholars, consumers, and policymakers. The ethical dimensions most commonly explored in the literature encompass human agency and oversight, followed by issues centered on privacy and data governance. Conversely, topics related to diversity, nondiscrimination, fairness, societal and environmental well-being, technical robustness, safety, transparency, and accountability receive comparatively less attention. This paper delineates the concrete operationalization of calls for public involvement in AI governance within the research sphere. It underscores the intricate interplay between ethical concerns, public involvement, and societal structures, including political and economic agendas, which serve to bolster technical proficiency and affirm the legitimacy of AI development in accordance with the institutional norms that underlie responsible research practices.

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1 Introduction

Current advances in the research, development, and application of artificial intelligence (AI) systems have yielded a far-reaching discourse on AI ethics that is accompanied by calls for AI technology to be democratically accountable and trustworthy from the publics’ Footnote 1 perspective [ 1 , 2 , 3 , 4 , 5 ]. Consequently, several ethics guidelines for AI have been released in recent years. As of early 2020, there were 167 AI ethics guidelines documents around the world [ 6 ]. Organizations such as the European Commission (EC), the Organization for Economic Co-operation and Development (OECD), and the United Nations Educational, Scientific and Cultural Organization (UNESCO) recognize that public participation is crucial for ensuring the responsible development and deployment of AI technologies, Footnote 2 emphasizing the importance of inclusivity, transparency, and democratic processes to effectively address the societal implications of AI [ 11 , 12 ]. These efforts were publicly announced as aiming to create a common understanding of ethical AI development and foster responsible practices that address societal concerns while maximizing AI’s potential benefits [ 13 , 14 ]. The concept of human-centric AI has emerged as a key principle in many of these regulatory initiatives, with the purposes of ensuring that human values are incorporated into the design of algorithms, that humans do not lose control over automated systems, and that AI is used in the service of humanity and the common good to improve human welfare and human rights [ 15 ]. Using the same rationale, the opacity and rapid diffusion of AI have prompted debate about how such technologies ought to be governed and which actors and values should be involved in shaping governance regimes [ 1 , 2 ].

While industry and business have traditionally tended to be seen as having no or little incentive to engage with ethics or in dialogue, AI leaders currently sponsor AI ethics [ 6 , 16 , 17 ]. However, some concerns call for ethics, public participation, and human-centric approaches in areas such as AI with high economic and political importance to be used within an instrumental rationale by the AI industry. A growing corpus of critical literature has conceived the development of AI ethics as efforts to reduce ethics to another form of industrial capital or to coopt and capture researchers as part of efforts to control public narratives [ 12 , 18 ]. According to some authors, one of the reasons why ethics is so appealing to many AI companies is to calm critical voices from the publics; therefore, AI ethics is seen as a way of gaining or restoring trust, credibility and support, as well as legitimation, while criticized practices are calmed down to maintain the agenda of industry and science [ 12 , 17 , 19 , 20 ].

Critical approaches also point out that despite regulatory initiatives explicitly invoking the need to incorporate human values into AI systems, they have the main objective of setting rules and standards to enable AI-based products and services to circulate in markets [ 20 , 21 , 22 ] and might serve to avoid or delay binding regulation [ 12 , 23 ]. Other critical studies argue that AI ethics fails to mitigate the racial, social, and environmental damage of AI technologies in any meaningful sense [ 24 ] and excludes alternative ethical practices [ 25 , 26 ]. As explained by Su [ 13 ], in a paper that considers the promise and perils of international human rights in AI governance, while human rights can serve as an authoritative source for holding AI developers accountable, its application to AI governance in practice shows a lack of effectiveness, an inability to effect structural change, and the problem of cooptation.

In a value analysis of AI national strategies, Wilson [ 5 ] concludes that the publics are primarily cast as recipients of AI’s abstract benefits, users of AI-driven services and products, a workforce in need of training and upskilling, or an important element for thriving democratic society that unlocks AI's potential. According to the author, when AI strategies articulate a governance role for the publics, it is more like an afterthought or rhetorical gesture than a clear commitment to putting “society-in-the-loop” into AI design and implementation [ 5 , pp. 7–8]. Another study of how public participation is framed in AI policy documents [ 4 ] shows that high expectations are assigned to public participation as a solution to address concerns about the concentration of power, increases in inequality, lack of diversity, and bias. However, in practice, this framing thus far gives little consideration to some of the challenges well known for researchers and practitioners of public participation with science and technology, such as the difficulty of achieving consensus among diverse societal views, the high resource requirements for public participation exercises, and the risks of capture by vested interests [ 4 , pp. 170–171]. These studies consistently reveal a noteworthy pattern: while references to public participation in AI governance are prevalent in the majority of AI national strategies, they tend to remain abstract and are often overshadowed by other roles, values, and policy concerns.

Some authors thus contended that the increasing demand to involve multiple stakeholders in AI governance, including the publics, signifies a discernible transformation within the sphere of science and technology policy. This transformation frequently embraces the framework of “responsible innovation”, Footnote 3 which emphasizes alignment with societal imperatives, responsiveness to evolving ethical, social, and environmental considerations, and the participation of the publics as well as traditionally defined stakeholders [ 3 , 28 ]. When investigating how the conception and promotion of public participation in European science and technology policies have evolved, Macq, Tancoine, and Strasser [ 29 ] distinguish between “participation in decision-making” (pertaining to science policy decisions or decisions on research topics) and “participation in knowledge and innovation-making”. They find that “while public participation had initially been conceived and promoted as a way to build legitimacy of research policy decisions by involving publics into decision-making processes, it is now also promoted as a way to produce better or more knowledge and innovation by involving publics into knowledge and innovation-making processes, and thus building legitimacy for science and technology as a whole” [ 29 , p. 508]. Although this shift in science and technology research policies has been noted, there exists a noticeable void in the literature in regard to understanding how concrete research practices incorporate public perspectives and embrace multistakeholder approaches, inclusion, and dialogue.

While several studies have delved into the framing of the publics’ role within AI governance in several instances (from Big Tech initiatives to hiring ethics teams and guidelines issued from multiple institutions to governments’ national policies related to AI development), discussing the underlying motivations driving the publics’ participation and the ethical considerations resulting from such involvement, there remains a notable scarcity of knowledge concerning how publicly voiced concerns are concretely translated into research efforts [ 30 , pp. 3–4, 31 , p. 8, 6]. To address this crucial gap, our scoping review endeavors to analyse the research landscape about the publics’ views on the ethical challenges of AI. Our primary objective is to uncover the motivations behind involving the publics in research initiatives, identify the segments of the publics that are considered in these studies, and illuminate the ethical concerns that warrant specific attention. Through this scoping review, we aim to enhance the understanding of the political and social backdrop within which debates and prior commitments regarding values and conditions for publics’ participation in matters related to science and technology are formulated and expressed [ 29 , 32 , 33 ] and which specific normative social commitments are projected and performed by institutional science [ 34 , p. 108, [ 35 , p. 856].

We followed the guidance for descriptive systematic scoping reviews by Levac et al. [ 36 ], based on the methodological framework developed by Arksey and O’Malley [ 37 ]. The steps of the review are listed below:

2.1 Stage 1: identifying the research question

The central question guiding this scoping review is the following: What motivations, publics and ethical issues emerge in research addressing the publics’ views on the ethical challenges of AI? We ask:

What motivations for engaging the publics with AI technologies are articulated?

Who are the publics invited?

Which ethical issues concerning AI technologies are perceived as needing the participation of the publics?

2.2 Stage 2: identifying relevant studies

A search of the publications on PubMed® and Web of Science™ was conducted on 19 May 2023, with no restriction set for language or time of publication, using the following search expression: (“AI” OR “artificial intelligence”) AND (“public” OR “citizen”) AND “ethics”. The search was followed by backwards reference tracking, examining the references of the selected publications based on full-text assessment.

2.3 Stage 3: study selection

The inclusion criteria allowed only empirical, peer-reviewed, original full-length studies written in English to explore publics’ views on the ethical challenges of AI as their main outcome. The exclusion criteria disallowed studies focusing on media discourses and texts. The titles of 1612 records were retrieved. After the removal of duplicates, 1485 records were examined. Two authors (HM and SS) independently screened all the papers retrieved initially, based on the title and abstract, and afterward, based on the full text. This was crosschecked and discussed in both phases, and perfect agreement was achieved.

The screening process is summarized in Fig.  1 . Based on title and abstract assessments, 1265 records were excluded because they were neither original full-length peer-reviewed empirical studies nor focused on the publics’ views on the ethical challenges of AI. Of the 220 fully read papers, 54 met the inclusion criteria. After backwards reference tracking, 10 papers were included, and the final review was composed of 64 papers.

Flowchart showing the search results and screening process for the scoping review of publics’ views on ethical challenges of AI

2.4 Stage 4: charting the data

A standardized data extraction sheet was initially developed by two authors (HM and SS) and completed by two coders (SS and LN), including both quantitative and qualitative data (Supplemental Table “Data Extraction”). We used MS Excel to chart the data from the studies.

The two coders independently charted the first 10 records, with any disagreements or uncertainties in abstractions being discussed and resolved by consensus. The forms were further refined and finalized upon consensus before completing the data charting process. Each of the remaining records was charted by one coder. Two meetings were held to ensure consistency in data charting and to verify accuracy. The first author (HM) reviewed the results.

Descriptive data for the characterization of studies included information about the authors and publication year, the country where the study was developed, study aims, type of research (quantitative, qualitative, or other), assessment of the publics’ views, and sample. The types of research participants recruited as publics were coded into 11 categories: developers of AI systems; managers from industry and business; representatives of governance bodies; policymakers; academics and researchers; students; professional groups; general public; local communities; patients/consumers; and other (specify).

Data on the main motivations for researching the publics’ views on the ethical challenges of AI were also gathered. Authors’ accounts of their motivations were synthesized into eight categories according to the coding framework proposed by Weingart and colleagues [ 33 ] concerning public engagement with science and technology-related issues: education (to inform and educate the public about AI, improving public access to scientific knowledge); innovation (to promote innovation, the publics are considered to be a valuable source of knowledge and are called upon to contribute to knowledge production, bridge building and including knowledge outside ‘formal’ ethics); legitimation (to promote public trust in and acceptance of AI, as well as of policies supporting AI); inspiration (to inspire and raise interest in AI, to secure a STEM-educated labor force); politicization (to address past political injustices and historical exclusion); democratization (to empower citizens to participate competently in society and/or to participate in AI); other (specify); and not clearly evident.

Based on the content analysis technique [ 38 ], ethical issues perceived as needing the participation of the publics were identified through quotations stated in the studies. These were then summarized in seven key ethical principles, according to the proposal outlined by the EC's Ethics Guidelines for Trustworthy AI [ 39 ]: human agency and oversight; technical robustness and safety; privacy and data governance; transparency; diversity, nondiscrimination and fairness; societal and environmental well-being; and accountability.

2.5 Stage 5: collating, summarizing, and reporting the results

The main characteristics of the 64 studies included can be found in Table  1 . Studies were grouped by type of research and ordered by the year of publication. The findings regarding the publics invited to participate are presented in Fig.  2 . The main motivations for engaging the publics with AI technologies and the ethical issues perceived as needing the participation of the publics are summarized in Tables  2 and 3 , respectively. The results are presented below in a narrative format, with complimentary tables and figures to provide a visual representation of key findings.

Publics invited to engage with issues framed as ethical challenges of AI

There are some methodological limitations in this scoping review that should be taken into account when interpreting the results. The use of only two search engines may preclude the inclusion of relevant studies, although supplemented by scanning the reference list of eligible studies. An in-depth analysis of the topics explored within each of the seven key ethical principles outlined by the EC's Ethics Guidelines for Trustworthy AI was not conducted. This assessment would lead to a detailed understanding of the publics’ views on ethical challenges of AI.

3.1 Study characteristics

Most of the studies were in recent years, with 35 of the 64 studies being published in 2022 and 2023. Journals were listed either on the databases related to Science Citation Index Expanded (n = 25) or the Social Science Citation Index (n = 23), with fewer journals indexed in the Emerging Sources Citation Index (n = 7) and the Arts and Humanities Citation Index (n = 2). Works covered a wide range of fields, including health and medicine (services, policy, medical informatics, medical ethics, public and environmental health); education; business, management and public administration; computer science; information sciences; engineering; robotics; communication; psychology; political science; and transportation. Beyond the general assessment of publics’ attitudes toward, preferences for, and expectations and concerns about AI, the publics’ views on ethical challenges of AI technologies have been studied mainly concerning healthcare and public services and less frequently regarding autonomous vehicles (AV), education, robotic technologies, and smart homes. Most of the studies (n = 47) were funded by research agencies, with 7 papers reporting conflicts of interest.

Quantitative research approaches have assessed the publics’ views on the ethical challenges of AI mainly through online or web-based surveys and experimental platforms, relying on Delphi studies, moral judgment studies, hypothetical vignettes, and choice-based/comparative conjoint surveys. The 25 qualitative studies collected data mainly by semistructured or in-depth interviews. Analysis of publicly available material reporting on AI-use cases, focus groups, a post hoc self-assessment, World Café, participatory research, and practice-based design research were used once or twice. Multi or mixed-methods studies relied on surveys with open-ended and closed questions, frequently combined with focus groups, in-depth interviews, literature reviews, expert opinions, examinations of relevant curriculum examples, tests, and reflexive writings.

The studies were performed (where stated) in a wide variety of countries, including the USA and Australia. More than half of the studies (n = 38) were conducted in a single country. Almost all studies used nonprobability sampling techniques. In quantitative studies, sample sizes varied from 2.3 M internet users in an online experimental platform study [ 40 ] to 20 participants in a Delphi study [ 41 ]. In qualitative studies, the samples varied from 123 participants in 21 focus groups [ 42 ] to six expert interviews [ 43 ]. In multi or mixed-methods studies, samples varied from 2036 participants [ 44 ] to 21 participants [ 45 ].

3.2 Motivations for engaging the publics

The qualitative synthesis of the motivations for researching the publics’ views on the ethical challenges of AI is presented in Table  2 and ordered by the number of studies referencing them in the scoping review. More than half of the studies (n = 37) addressed a single motivation. Innovation (n = 33) and legitimation (n = 29) proved to have the highest relevance as motivations for engaging the publics in the ethical challenges of AI technologies, as articulated in 15 studies. Additional motivations are rooted in education (n = 13), democratization (n = 11), and inspiration (n = 9). Politicization was mentioned in five studies. Although they were not authors’ motivations, few studies were found to have educational [ 46 , 47 ], democratization [ 48 , 49 ], and legitimation or inspirations effects [ 50 ].

To consider the publics as a valuable source of knowledge that can add real value to innovation processes in both the private and public sectors was the most frequent motivation mentioned in the literature. The call for public participation is rooted in the aspiration to add knowledge outside “formal” ethics at three interrelated levels. First, at a societal level, by asking what kind of AI we want as a society based on novel experiments on public policy preferences [ 51 ] and on the study of public perceptions, values, and concerns regarding AI design, development, and implementation in domains such as health care [ 46 , 52 , 53 , 54 , 55 ], public and social services [ 49 , 56 , 57 , 58 ], AV [ 59 , 60 ] and journalism [ 61 ]. Second, at a practical level, the literature provides insights into the perceived usefulness of AI applications [ 62 , 63 ] and choices between boosting developers’ voluntary adoption of ethical standards or imposing ethical standards via regulation and oversight [ 64 ], as well as suggesting specific guidance for the development and use of AI systems [ 65 , 66 , 67 ]. Finally, at a theoretical level, literature expands the social-technical perspective [ 68 ] and motivated-reasoning theory [ 69 ].

Legitimation was also a frequent motivation for engaging the publics. It was underpinned by the need for public trust in and social licences for implementing AI technologies. To ensure the long-term social acceptability of AI as a trustworthy technology [ 70 , 71 ] was perceived as essential to support its use and to justify its implementation. In one study [ 72 ], the authors developed an AI ethics scale to quantify how AI research is accepted in society and which area of ethical, legal, and social issues (ELSI) people are most concerned with. Public trust in and acceptance of AI is claimed by social institutions such as governments, private sectors, industry bodies, and the science community, behaving in a trustworthy manner, respecting public concerns, aligning with societal values, and involving members of the publics in decision-making and public policy [ 46 , 48 , 73 , 74 , 75 ], as well as in the responsible design and integration of AI technologies [ 52 , 76 , 77 ].

Education, democratization, and inspiration had a more modest presence as motivations to explore the publics’ views on the ethical challenges of AI. Considering the emergence of new roles and tasks related to AI, the literature has pointed to the public need to ensure the safe use of AI technologies by incorporating ethics and career futures into the education, preparation, and training of both middle school and university students and the current and future health workforce. Improvements in education and guidance for developers and older adults were also noticed. The views of the publics on what needs to be learned or how this learning may be supported or assessed were perceived as crucial. In one study [ 78 ], the authors developed strategies that promote learning related to AI through collaborative media production, connecting computational thinking to civic issues and creative expression. In another study [ 79 ], real-world scenarios were successfully used as a novel approach to teaching AI ethics. Rhim et al. [ 76 ] provided AV moral behavior design guidelines for policymakers, developers, and the publics by reducing the abstractness of AV morality.

Studies motivated by democratization promoted broader public participation in AI, aiming to empower citizens both to express their understandings, apprehensions, and concerns about AI [ 43 , 78 , 80 , 81 ] and to address ethical issues in AI as critical consumers, (potential future) developers of AI technologies or would-be participants in codesign processes [ 40 , 43 , 45 , 78 , 82 , 83 ]. Understanding the publics’ views on the ethical challenges of AI is expected to influence companies and policymakers [ 40 ]. In one study [ 45 ], the authors explored how a digital app might support citizens’ engagement in AI governance by informing them, raising public awareness, measuring publics’ attitudes and supporting collective decision-making.

Inspiration revolved around three main motivations: to raise public interest in AI [ 46 , 48 ]; to guide future empirical and design studies [ 79 ]; and to promote developers’ moral awareness through close collaboration between all those involved in the implementation, use, and design of AI technologies [ 46 , 61 , 78 , 84 , 85 ].

Politicization was the less frequent motivation reported in the literature for engaging the publics. Recognizing the need for mitigation of social biases [ 86 ], public participation to address historically marginalized populations [ 78 , 87 ], and promoting social equity [ 79 ] were the highlighted motives.

3.3 The invited publics

Study participants were mostly the general public and professional groups, followed by developers of AI systems, managers from industry and business, students, academics and researchers, patients/consumers, and policymakers (Fig.  2 ). The views of local communities and representatives of governance bodies were rarely assessed.

Representative samples of the general public were used in five papers related to studies conducted in the USA [ 88 ], Denmark [ 73 ], Germany [ 48 ], and Austria [ 49 , 63 ]. The remaining random or purposive samples from the general public comprised mainly adults and current and potential users of AI products and services, with few studies involving informal caregivers or family members of patients (n = 3), older people (n = 2), and university staff (n = 2).

Samples of professional groups included mainly healthcare professionals (19 out of 24 studies). Educators, law enforcement, media practitioners, and GLAM professionals (galleries, libraries, archives, and museums) were invited once.

3.4 Ethical issues

The ethical issues concerning AI technologies perceived as needing the participation of the publics are depicted in Table  3 . They were mapped by measuring the number of studies referencing them in the scoping review. Human agency and oversight (n = 55) was the most frequent ethical aspect that was studied in the literature, followed by those centered on privacy and data governance (n = 43). Diversity, nondiscrimination and fairness (n = 39), societal and environmental well-being (n = 39), technical robustness and safety (n = 38), transparency (n = 35), and accountability (n = 31) were less frequently discussed.

The concerns regarding human agency and oversight were the replacement of human beings by AI technologies and deskilling [ 47 , 55 , 67 , 74 , 75 , 89 , 90 ]; the loss of autonomy, critical thinking, and innovative capacities [ 50 , 58 , 61 , 77 , 78 , 83 , 85 , 90 ]; the erosion of human judgment and oversight [ 41 , 70 , 91 ]; and the potential for (over)dependence on technology and “oversimplified” decisions [ 90 ] due to the lack of publics’ expertise in judging and controlling AI technologies [ 68 ]. Beyond these ethical challenges, the following contributions of AI systems to empower human beings were noted: more fruitful and empathetic social relationships [ 47 , 68 , 90 ]; enhancing human capabilities and quality of life [ 68 , 70 , 74 , 83 , 92 ]; improving efficiency and productivity at work [ 50 , 53 , 62 , 65 , 83 ] by reducing errors [ 77 ], relieving the burden of professionals and/or increasing accuracy in decisions [ 47 , 55 , 90 ]; and facilitating and expanding access to safe and fair healthcare [ 42 , 53 , 54 ] through earlier diagnosis, increased screening and monitoring, and personalized prescriptions [ 47 , 90 ]. To foster human rights, allowing people to make informed decisions, the last say was up to the person themselves [ 42 , 43 , 46 , 55 , 64 , 67 , 73 , 76 ]. People should determine where and when to use automated functions and which functions to use [ 44 , 54 ], developing “job sharing” arrangements with machines and humans complementing and enriching each other [ 56 , 65 , 90 ]. The literature highlights the need to build AI systems that are under human control [ 48 , 70 ] whether to confirm or to correct the AI system’s outputs and recommendations [ 66 , 90 ]. Proper oversight mechanisms were seen as crucial to ensure accuracy and completeness, with divergent views about who should be involved in public participation approaches [ 86 , 87 ].

Data sharing and/or data misuse were considered the major roadblocks regarding privacy and data governance, with some studies pointing out distrust of participants related to commercial interests in health data [ 55 , 90 , 93 , 94 , 95 ] and concerns regarding risks of information getting into the hands of hackers, banks, employers, insurance companies, or governments [ 66 ]. As data are the backbone of AI, secure methods of data storage and protection are understood as needing to be provided from the input to the output data. Recognizing that in contemporary societies, people are aware of the consequences of smartphone use resulting in the minimization of privacy concerns [ 93 ], some studies have focused on the impacts of data breaches and loss of privacy and confidentiality [ 43 , 45 , 46 , 60 , 62 , 80 ] in relation to health-sensitive personal data [ 46 , 93 ], potentially affecting more vulnerable populations, such as senior citizens and mentally ill patients [ 82 , 90 ] as well as those at young ages [ 50 ], and when journalistic organizations collect user data to provide personalized news suggestions [ 61 ]. The need to find a balance between widening access to data and ensuring confidentiality and respect for privacy [ 53 ] was often expressed in three interrelated terms: first, the ability of data subjects to be fully informed about how data will be used and given the option of providing informed consent [ 46 , 58 , 78 ] and controlling personal information about oneself [ 57 ]; second, the need for regulation [ 52 , 65 , 87 ], with one study reporting that AI developers complain about the complexity, slowness, and obstacles created by regulation [ 64 ]; and last, the testing and certification of AI-enabled products and services [ 71 ]. The study by De Graaf et al. [ 91 ] discussed the robots’ right to store and process the data they collect, while Jenkins and Draper [ 42 ] explored less intrusive ways in which the robot could use information to report back to carers about the patient’s adherence to healthcare.

Studies discussing diversity, nondiscrimination, and fairness have pointed to the development of AI systems that reflect and reify social inequalities [ 45 , 78 ] through nonrepresentative datasets [ 55 , 58 , 96 , 97 ] and algorithmic bias [ 41 , 45 , 85 , 98 ] that might benefit some more than others. This could have multiple negative consequences for different groups based on ethnicity, disease, physical disability, age, gender, culture, or socioeconomic status [ 43 , 55 , 58 , 78 , 82 , 87 ], from the dissemination of hate speech [ 79 ] to the exacerbation of discrimination, which negatively impacts peace and harmony within society [ 58 ]. As there were cross-country differences and issue variations in the publics’ views of discriminatory bias [ 51 , 72 , 73 ], fostering diversity, inclusiveness, and cultural plurality [ 61 ] was perceived as crucial to ensure the transferability/effectiveness of AI systems in all social groups [ 60 , 94 ]. Diversity, nondiscrimination, and fairness were also discussed as a means to help reduce health inequalities [ 41 , 67 , 90 ], to compensate for human preconceptions about certain individuals [ 66 ], and to promote equitable distribution of benefits and burdens [ 57 , 71 , 80 , 93 ], namely, supporting access by all to the same updated and high-quality AI systems [ 50 ]. In one study [ 83 ], students provided constructive solutions to build an unbiased AI system, such as using a dataset that includes a diverse dataset engaging people of different ages, genders, ethnicities, and cultures. In another study [ 86 ], participants recommended diverse approaches to mitigate algorithmic bias, from open disclosure of limitations to consumer and patient engagement, representation of marginalized groups, incorporation of equity considerations into sampling methods and legal recourse, and identification of a wide range of stakeholders who may be responsible for addressing AI bias: developers, healthcare workers, manufacturers and vendors, policymakers and regulators, AI researchers and consumers.

Impacts on employment and social relationships were considered two major ethical challenges regarding societal and environmental well-being. The literature has discussed tensions between job creation [ 51 ] and job displacement [ 42 , 90 ], efficiency [ 90 ], and deskilling [ 57 ]. The concerns regarding future social relationships were the loss of empathy, humanity, and/or sensitivity [ 52 , 66 , 90 , 99 ]; isolation and fewer social connections [ 42 , 47 , 90 ]; laziness [ 50 , 83 ]; anxious counterreactions [ 83 , 99 ]; communication problems [ 90 ]; technology dependence [ 60 ]; plagiarism and cheating in education [ 50 ]; and becoming too emotionally attached to a robot [ 65 ]. To overcome social unawareness [ 56 ] and lack of acceptance [ 65 ] due to financial costs [ 56 , 90 ], ecological burden [ 45 ], fear of the unknown [ 65 , 83 ] and/or moral issues [ 44 , 59 , 100 ], AI systems need to provide public benefit sharing [ 55 ], consider discrepancies between public discourse about AI and the utility of the tools in real-world settings and practices [ 53 ], conform to the best standards of sustainability and address climate change and environmental justice [ 60 , 71 ]. Successful strategies in promoting the acceptability of robots across contexts included an approachable and friendly looking as possible, but not too human-like [ 49 , 65 ], and working with, rather than in competition, with humans [ 42 ].

The publics were invited to participate in the following ethical issues related to technical robustness and safety: usability, reliability, liability, and quality assurance checks of AI tools [ 44 , 45 , 55 , 62 , 99 ]; validity of big data analytic tools [ 87 ]; the degree to which an AI system can perform tasks without errors or mistakes [ 50 , 57 , 66 , 84 , 90 , 93 ]; and needed resources to perform appropriate (cyber)security [ 62 , 101 ]. Other studies approached the need to consider both material and normative concerns of AI applications [ 51 ], namely, assuring that AI systems are developed responsibly with proper consideration of risks [ 71 ] and sufficient proof of benefits [ 96 ]. One study [ 64 ] highlighted that AI developers tend to be reluctant to recognize safety issues, bias, errors, and failures, and when they do so, they do so in a selective manner and in their terms by adopting positive-sounding professional jargon as AI robustness.

Some studies recognized the need for greater transparency that reduces the mystery and opaqueness of AI systems [ 71 , 82 , 101 ] and opens its “black box” [ 64 , 71 , 98 ]. Clear insights about “what AI is/is not” and “how AI technology works” (definition, applications, implications, consequences, risks, limitations, weaknesses, threats, rewards, strengths, opportunities) were considered as needed to debunk the myth about AI as an independent entity [ 53 ] and for providing sufficient information and understandable explanations of “what’s happening” to society and individuals [ 43 , 48 , 72 , 73 , 78 , 102 ]. Other studies considered that people, when using AI tools, should be made fully aware that these AI devices are capturing and using their data [ 46 ] and how data are collected [ 58 ] and used [ 41 , 46 , 93 ]. Other transparency issues reported in the literature included the need for more information about the composition of data training sets [ 55 ], how algorithms work [ 51 , 55 , 84 , 94 , 97 ], how AI makes a decision [ 57 ] and the motivations for that decision [ 98 ]. Transparency requirements were also addressed as needing the involvement of multiple stakeholders: one study reported that transparency requirements should be seen as a mediator of debate between experts, citizens, communities, and stakeholders [ 87 ] and cannot be reduced to a product feature, avoiding experiences where people feel overwhelmed by explanations [ 98 ] or “too much information” [ 66 ].

Accountability was perceived by the publics as an important ethical issue [ 48 ], while developers expressed mixed attitudes, from moral disengagement to a sense of responsibility and moral conflict and uncertainty [ 85 ]. The literature has revealed public skepticism regarding accountability mechanisms [ 93 ] and criticism about the shift of responsibility away from tech industries that develop and own AI technologies [ 53 , 68 ], as it opens space for users to assume their own individual responsibility [ 78 ]. This was the case in studies that explored accountability concerns regarding the assignment of fault and responsibility for car accidents using self-driving technology [ 60 , 76 , 77 , 88 ]. Other studies considered that more attention is needed to scrutinize each application across the AI life cycle [ 41 , 71 , 94 ], to explainability of AI algorithms that provide to the publics the cause of AI outcomes [ 58 ], and to regulations that assign clear responsibility concerning litigation and liability [ 52 , 89 , 101 , 103 ].

4 Discussion

Within the realm of research studies encompassed in the scoping review, the contemporary impetus for engaging the publics in ethical considerations related to AI predominantly revolves around two key motivations: innovation and legitimation. This might be explained by the current emphasis on responsible innovation, which values the publics’ participation in knowledge and innovation-making [ 29 ] within a prioritization of the instrumental role of science for innovation and economic return [ 33 ]. Considering the publics as a valuable source of knowledge that should be called upon to contribute to knowledge innovation production is underpinned by the desire for legitimacy, specifically centered around securing the publics’ endorsement of scientific and technological advancements [ 33 , 104 ]. Approaching the publics’ views on the ethical challenges of AI can also be used as a form of risk prevention to reduce conflict and close vital debates in contention areas [ 5 , 34 , 105 ].

A second aspect that stood out in this finding is a shift in the motivations frequently reported as central for engaging the publics with AI technologies. Previous studies analysing AI national policies and international guidelines addressing AI governance [ 3 , 4 , 5 ] and a study analysing science communication journals [ 33 ] highlighted education, inspiration and democratization as the most prominent motivations. Our scoping review did not yield similar findings, which might signal a departure, in science policy related to public participation, from the past emphasis on education associated with the deficit model of public understanding of science and democratization of the model of public engagement with science [ 106 , 107 ].

The underlying motives for the publics’ engagement raise the question of the kinds of publics it addresses, i.e., who are the publics that are supposed to be recruited as research participants [ 32 ]. Our findings show a prevalence of the general public followed by professional groups and developers of AI systems. The wider presence of the general public indicates not only what Hagendijk and Irwin [ 32 , p. 167] describe as a fashionable tendency in policy circles since the late 1990s, and especially in Europe, focused on engaging 'the public' in scientific and technological change but also the avoidance of the issues of democratic representation [ 12 , 18 ]. Additionally, the unspecificity of the “public” does not stipulate any particular action [ 24 ] that allows for securing legitimacy for and protecting the interests of a wide range of stakeholders [ 19 , 108 ] while bringing the risk of silencing the voices of the very publics with whom engagement is sought [ 33 ]. The focus on approaching the publics’ views on the ethical challenges of AI through the general public also demonstrates how seeking to “lay” people’s opinions may be driven by a desire to promote public trust and acceptance of AI developments, showing how science negotiates challenges and reinstates its authority [ 109 ].

While this strategy is based on nonscientific audiences or individuals who are not associated with any scientific discipline or area of inquiry as part of their professional activities, the converse strategy—i.e., involving professional groups and AI developers—is also noticeable in our findings. This suggests that technocratic expert-dominated approaches coexist with a call for more inclusive multistakeholder approaches [ 3 ]. This coexistence is reinforced by the normative principles of the “responsible innovation” framework, in particular the prescription that innovation should include the publics as well as traditionally defined stakeholders [ 3 , 110 ], whose input has become so commonplace that seeking the input of laypeople on emerging technologies is sometimes described as a “standard procedure” [ 111 , p. 153].

In the body of literature included in the scoping review, human agency and oversight emerged as the predominant ethical dimension under investigation. This finding underscores the pervasive significance attributed to human centricity, which is progressively integrated into public discourses concerning AI, innovation initiatives, and market-driven endeavours [ 15 , 112 ]. In our perspective, the importance given to human-centric AI is emblematic of the “techno-regulatory imaginary” suggested by Rommetveit and van Dijk [ 35 ] in their study about privacy engineering applied in the European Union’s General Data Protection Regulation. This term encapsulates the evolving collective vision and conceptualization of the role of technology in regulatory and oversight contexts. At least two aspects stand out in the techno-regulatory imaginary, as they are meant to embed technoscience in societally acceptable ways. First, it reinstates pivotal demarcations between humans and nonhumans while concurrently producing intensified blurring between these two realms. Second, the potential resolutions offered relate to embedding fundamental rights within the structural underpinnings of technological architectures [ 35 ].

Following human agency and oversight, the most frequent ethical issue discussed in the studies contained in our scoping review was privacy and data governance. Our findings evidence additional central aspects of the “techno-regulatory imaginary” in the sense that instead of the traditional regulatory sites, modes of protecting privacy and data are increasingly located within more privatized and business-oriented institutions [ 6 , 35 ] and crafted according to a human-centric view of rights. The focus on secure ways of data storage and protection as in need to be provided from the input to the output data, the testing and certification of AI-enabled products and services, the risks of data breaches, and calls for finding a balance between widening access to data and ensuring confidentiality and respect for privacy, exhibited by many studies in this scoping review, portray an increasing framing of privacy and data protection within technological and standardization sites. This tendency shows how forms of expertise for privacy and data protection are shifting away from traditional regulatory and legal professionals towards privacy engineers and risk assessors in information security and software development. Another salient element to highlight pertains to the distribution of responsibility for privacy and data governance [ 6 , 113 ] within the realm of AI development through engagement with external stakeholders, including users, governmental bodies, and regulatory authorities. It extends from an emphasis on issues derived from data sharing and data misuse to facilitating individuals to exercise control over their data and privacy preferences and to advocating for regulatory frameworks that do not impede the pace of innovation. This distribution of responsibility shared among the contributions and expectations of different actors is usually convoked when the operationalization of ethics principles conflicts with AI deployment [ 6 ]. In this sense, privacy and data governance are reconstituted as a “normative transversal” [ 113 , p. 20], both of which work to stabilize or close controversies, while their operationalization does not modify any underlying operations in AI development.

Diversity, nondiscrimination and fairness, societal and environmental well-being, technical robustness and safety, transparency, and accountability were the ethical issues less frequently discussed in the studies included in this scoping review. In contrast, ethical issues of technical robustness and safety, transparency, and accountability “are those for which technical fixes can be or have already been developed” and “implemented in terms of technical solutions” [ 12 , p. 103]. The recognition of issues related to technical robustness and safety expresses explicit admissions of expert ignorance, error, or lack of control, which opens space for politics of “optimization of algorithms” [ 114 , p. 17] while reinforcing “strategic ignorance” [ 114 , p. 89]. In the words of the sociologist Linsey McGoey, strategic ignorance refers to “any actions which mobilize, manufacture or exploit unknowns in a wider environment to avoid liability for earlier actions” [ 115 , p. 3].

According to the analysis of Jobin et al. [ 11 ] of the global landscape of existing ethics guidelines for AI, transparency comprising efforts to increase explainability, interpretability, or other acts of communication and disclosure is the most prevalent principle in the current literature. Transparency gains high relevance in ethics guidelines because this principle has become a pro-ethical condition “enabling or impairing other ethical practices or principles” [Turilli and Floridi 2009, [ 11 ], p. 14]. Our findings highlight transparency as a crucial ethical concern for explainability and disclosure. However, as emphasized by Ananny and Crawford [ 116 , p. 973], there are serious limitations to the transparency ideal in making black boxes visible (i.e., disclosing and explaining algorithms), since “being able to see a system is sometimes equated with being able to know how it works and governs it—a pattern that recurs in recent work about transparency and computational systems”. The emphasis on transparency mirrors Aradau and Blanke’s [ 114 ] observation that Big Tech firms are creating their version of transparency. They are prompting discussions about their data usage, whether it is for “explaining algorithms” or addressing bias and discrimination openly.

The framing of ethical issues related to accountability, as elucidated by the studies within this scoping review, manifests as a commitment to ethical conduct and the transparent allocation of responsibility and legal obligations in instances where the publics encounters algorithmic deficiencies, glitches, or other imperfections. Within this framework, accountability becomes intricately intertwined with the notion of distributed responsibility, as expounded upon in our examination of how the literature addresses challenges in privacy and data governance. Simultaneously, it converges with our discussion on optimizing algorithms concerning ethical concerns on technical robustness and safety by which AI systems are portrayed as fallible yet eternally evolving towards optimization. As astutely observed by Aradau and Blanke [ 114 , p. 171], “forms of accountability through error enact algorithmic systems as fallible but ultimately correctable and therefore always desirable. Errors become temporary malfunctions, while the future of algorithms is that of indefinite optimization”.

5 Conclusion

This scoping review of how publics' views on ethical challenges of AI are framed, articulated, and concretely operationalized in the research sector shows that ethical issues and publics formation are closely entangled with symbolic and social orders, including political and economic agendas and visions. While Steinhoff [ 6 ] highlights the subordinated nature of AI ethics within an innovation network, drawing on insights from diverse sources beyond Big Tech, we assert that this network is dynamically evolving towards greater hybridity and boundary fusion. In this regard, we extend Steinhoff's argument by emphasizing the imperative for a more nuanced understanding of how this network operates within diverse contexts. Specifically, within the research sector, it operates through a convergence of boundaries, engaging human and nonhuman entities and various disciplines and stakeholders. Concurrently, the advocacy for diversity and inclusivity, along with the acknowledgement of errors and flaws, serves to bolster technical expertise and reaffirm the establishment of order and legitimacy in alignment with the institutional norms underpinning responsible research practices.

Our analysis underscores the growing importance of involving the publics in AI knowledge creation and innovation, both to secure public endorsement and as a tool for risk prevention and conflict mitigation. We observe two distinct approaches: one engaging nonscientific audiences and the other involving professional groups and AI developers, emphasizing the need for inclusivity while safeguarding expert knowledge. Human-centred approaches are gaining prominence, emphasizing the distinction and blending of human and nonhuman entities and embedding fundamental rights in technological systems. Privacy and data governance emerge as the second most prevalent ethical concern, shifting expertise away from traditional regulatory experts to privacy engineers and risk assessors. The distribution of responsibility for privacy and data governance is a recurring theme, especially in cases of ethical conflicts with AI deployment. However, there is a notable imbalance in attention, with less focus on diversity, nondiscrimination, fairness, societal, and environmental well-being, compared to human-centric AI, privacy, and data governance being managed through technical fixes. Last, acknowledging technical robustness and safety, transparency, and accountability as foundational ethics principles reveals an openness to expert limitations, allowing room for the politics of algorithm optimization, framing AI systems as correctable and perpetually evolving.

Data availability

This manuscript has data included as electronic supplementary material. The dataset constructed by the authors, resulting from a search of publications on PubMed ® and Web of Science™, analysed in the current study, is not publicly available. But it can be available from the corresponding author on reasonable request.

In this article, we will employ the term "publics" rather than the singular "public" to delineate our viewpoint concerning public participation in AI. Our option is meant to acknowledge that there are no uniform, monolithic viewpoints or interests. From our perspective, the term "publics" allows for a more nuanced understanding of the various groups, communities, and individuals who may have different attitudes, beliefs, and concerns regarding AI. This choice may differ from the terminology employed in the referenced literature.

The following examples are particularly illustrative of the multiplicity of organizations emphasizing the need for public participation in AI. The OECD Recommendations of the Council on AI specifically emphasizes the importance of empowering stakeholders considering essential their engagement to adoption of trustworthy [ 7 , p. 6]. The UNESCO Recommendation on the Ethics of AI emphasizes that public awareness and understanding of AI technologies should be promoted (recommendation 44) and it encourages governments and other stakeholders to involve the publics in AI decision-making processes (recommendation 47) [ 8 , p. 23]. The European Union (EU) White Paper on AI [ 9 , p. 259] outlines the EU’s approach to AI, including the need for public consultation and engagement. The Ethics Guidelines for Trustworthy AI [ 10 , pp. 19, 239], developed by the High-Level Expert Group on AI (HLEG) appointed by the EC, emphasize the importance of public participation and consultation in the design, development, and deployment of AI systems.

“Responsible Innovation” (RI) and “Responsible Research and Innovation” (RRI) have emerged in parallel and are often used interchangeably, but they are not the same thing [ 27 , 28 ]. RRI is a policy-driven discourse that emerged from the EC in the early 2010s, while RI emerged largely from academic roots. For this paper, we will not consider the distinctive features of each discourse, but instead focus on the common features they share.

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Acknowledgements

The authors would like to express their gratitude to Rafaela Granja (CECS, University of Minho) for her insightful support in an early stage of preparation of this manuscript, and to the AIDA research netwrok for the inspiring debates.

Open access funding provided by FCT|FCCN (b-on). Helena Machado and Susana Silva did not receive funding to assist in the preparation of this work. Laura Neiva received funding from FCT—Fundação para a Ciência e a Tecnologia, I.P., under a PhD Research Studentships (ref.2020.04764.BD), and under the project UIDB/00736/2020 (base funding) and UIDP/00736/2020 (programmatic funding).

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Machado, H., Silva, S. & Neiva, L. Publics’ views on ethical challenges of artificial intelligence: a scoping review. AI Ethics (2023). https://doi.org/10.1007/s43681-023-00387-1

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