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Literature Review

• What is a Literature Review?
• What is NOT a Literature Review?
• Purposes of a Literature Review
• Types of Literature Reviews
• Literature Reviews vs. Systematic Reviews
• Systematic vs. Meta-Analysis

Literature Review  is a comprehensive survey of the works published in a particular field of study or line of research, usually over a specific period of time, in the form of an in-depth, critical bibliographic essay or annotated list in which attention is drawn to the most significant works.

Also, we can define a literature review as the collected body of scholarly works related to a topic:

• Summarizes and analyzes previous research relevant to a topic
• Includes scholarly books and articles published in academic journals
• Can be an specific scholarly paper or a section in a research paper

The objective of a Literature Review is to find previous published scholarly works relevant to an specific topic

• Help gather ideas or information
• Keep up to date in current trends and findings
• Help develop new questions

A literature review is important because it:

• Explains the background of research on a topic.
• Demonstrates why a topic is significant to a subject area.
• Helps focus your own research questions or problems
• Discovers relationships between research studies/ideas.
• Suggests unexplored ideas or populations
• Identifies major themes, concepts, and researchers on a topic.
• Tests assumptions; may help counter preconceived ideas and remove unconscious bias.
• Identifies critical gaps, points of disagreement, or potentially flawed methodology or theoretical approaches.
• Indicates potential directions for future research.

All content in this section is from Literature Review Research from Old Dominion University 

Keep in mind the following, a literature review is NOT:

Not an essay 

Not an annotated bibliography  in which you summarize each article that you have reviewed.  A literature review goes beyond basic summarizing to focus on the critical analysis of the reviewed works and their relationship to your research question.

Not a research paper   where you select resources to support one side of an issue versus another.  A lit review should explain and consider all sides of an argument in order to avoid bias, and areas of agreement and disagreement should be highlighted.

A literature review serves several purposes. For example, it

• provides thorough knowledge of previous studies; introduces seminal works.
• helps focus one’s own research topic.
• identifies a conceptual framework for one’s own research questions or problems; indicates potential directions for future research.
• suggests previously unused or underused methodologies, designs, quantitative and qualitative strategies.
• identifies gaps in previous studies; identifies flawed methodologies and/or theoretical approaches; avoids replication of mistakes.
• helps the researcher avoid repetition of earlier research.
• suggests unexplored populations.
• determines whether past studies agree or disagree; identifies controversy in the literature.
• tests assumptions; may help counter preconceived ideas and remove unconscious bias.

As Kennedy (2007) notes*, it is important to think of knowledge in a given field as consisting of three layers. First, there are the primary studies that researchers conduct and publish. Second are the reviews of those studies that summarize and offer new interpretations built from and often extending beyond the original studies. Third, there are the perceptions, conclusions, opinion, and interpretations that are shared informally that become part of the lore of field. In composing a literature review, it is important to note that it is often this third layer of knowledge that is cited as "true" even though it often has only a loose relationship to the primary studies and secondary literature reviews.

Given this, while literature reviews are designed to provide an overview and synthesis of pertinent sources you have explored, there are several approaches to how they can be done, depending upon the type of analysis underpinning your study. Listed below are definitions of types of literature reviews:

Argumentative Review      This form examines literature selectively in order to support or refute an argument, deeply imbedded assumption, or philosophical problem already established in the literature. The purpose is to develop a body of literature that establishes a contrarian viewpoint. Given the value-laden nature of some social science research [e.g., educational reform; immigration control], argumentative approaches to analyzing the literature can be a legitimate and important form of discourse. However, note that they can also introduce problems of bias when they are used to to make summary claims of the sort found in systematic reviews.

Integrative Review      Considered a form of research that reviews, critiques, and synthesizes representative literature on a topic in an integrated way such that new frameworks and perspectives on the topic are generated. The body of literature includes all studies that address related or identical hypotheses. A well-done integrative review meets the same standards as primary research in regard to clarity, rigor, and replication.

Historical Review      Few things rest in isolation from historical precedent. Historical reviews are focused on examining research throughout a period of time, often starting with the first time an issue, concept, theory, phenomena emerged in the literature, then tracing its evolution within the scholarship of a discipline. The purpose is to place research in a historical context to show familiarity with state-of-the-art developments and to identify the likely directions for future research.

Methodological Review      A review does not always focus on what someone said [content], but how they said it [method of analysis]. This approach provides a framework of understanding at different levels (i.e. those of theory, substantive fields, research approaches and data collection and analysis techniques), enables researchers to draw on a wide variety of knowledge ranging from the conceptual level to practical documents for use in fieldwork in the areas of ontological and epistemological consideration, quantitative and qualitative integration, sampling, interviewing, data collection and data analysis, and helps highlight many ethical issues which we should be aware of and consider as we go through our study.

Systematic Review      This form consists of an overview of existing evidence pertinent to a clearly formulated research question, which uses pre-specified and standardized methods to identify and critically appraise relevant research, and to collect, report, and analyse data from the studies that are included in the review. Typically it focuses on a very specific empirical question, often posed in a cause-and-effect form, such as "To what extent does A contribute to B?"

Theoretical Review      The purpose of this form is to concretely examine the corpus of theory that has accumulated in regard to an issue, concept, theory, phenomena. The theoretical literature review help establish what theories already exist, the relationships between them, to what degree the existing theories have been investigated, and to develop new hypotheses to be tested. Often this form is used to help establish a lack of appropriate theories or reveal that current theories are inadequate for explaining new or emerging research problems. The unit of analysis can focus on a theoretical concept or a whole theory or framework.

* Kennedy, Mary M. "Defining a Literature."  Educational Researcher  36 (April 2007): 139-147.

All content in this section is from The Literature Review created by Dr. Robert Larabee USC

Robinson, P. and Lowe, J. (2015),  Literature reviews vs systematic reviews.  Australian and New Zealand Journal of Public Health, 39: 103-103. doi: 10.1111/1753-6405.12393

What's in the name? The difference between a Systematic Review and a Literature Review, and why it matters . By Lynn Kysh from University of Southern California

Systematic review or meta-analysis?

A  systematic review  answers a defined research question by collecting and summarizing all empirical evidence that fits pre-specified eligibility criteria.

A  meta-analysis  is the use of statistical methods to summarize the results of these studies.

Systematic reviews, just like other research articles, can be of varying quality. They are a significant piece of work (the Centre for Reviews and Dissemination at York estimates that a team will take 9-24 months), and to be useful to other researchers and practitioners they should have:

• clearly stated objectives with pre-defined eligibility criteria for studies
• explicit, reproducible methodology
• a systematic search that attempts to identify all studies
• assessment of the validity of the findings of the included studies (e.g. risk of bias)
• systematic presentation, and synthesis, of the characteristics and findings of the included studies

Not all systematic reviews contain meta-analysis. 

Meta-analysis is the use of statistical methods to summarize the results of independent studies. By combining information from all relevant studies, meta-analysis can provide more precise estimates of the effects of health care than those derived from the individual studies included within a review.  More information on meta-analyses can be found in  Cochrane Handbook, Chapter 9 .

A meta-analysis goes beyond critique and integration and conducts secondary statistical analysis on the outcomes of similar studies.  It is a systematic review that uses quantitative methods to synthesize and summarize the results.

An advantage of a meta-analysis is the ability to be completely objective in evaluating research findings.  Not all topics, however, have sufficient research evidence to allow a meta-analysis to be conducted.  In that case, an integrative review is an appropriate strategy. 

Some of the content in this section is from Systematic reviews and meta-analyses: step by step guide created by Kate McAllister.

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• TECHNOLOGY FEATURE
• 01 September 2021
• Clarification 10 September 2021

Drowning in the literature? These smart software tools can help

• David Matthews 0

David Matthews is a freelance writer based in Berlin.

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Every time Eddie Smolyansky had a few moments to himself, he tried to stay abreast of new publications in his field. But by 2016, the computer-vision researcher, who is based in Tel Aviv, Israel, was receiving hundreds of automated literature recommendations per day. “At some point the bathroom breaks weren’t enough,” he says. The recommendations were “way too much, and impossible to keep up with”.

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Nature 597 , 141-142 (2021)

doi: https://doi.org/10.1038/d41586-021-02346-4

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Clarification 10 September 2021 : The text of this Technology feature has been modifed to clarify that ResearchGate and SpringerNature are in a content-sharing partnership.

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How to undertake a literature search: a step-by-step guide

Affiliation.

• 1 Literature Search Specialist, Library and Archive Service, Royal College of Nursing, London.
• PMID: 32279549
• DOI: 10.12968/bjon.2020.29.7.431

Undertaking a literature search can be a daunting prospect. Breaking the exercise down into smaller steps will make the process more manageable. This article suggests 10 steps that will help readers complete this task, from identifying key concepts to choosing databases for the search and saving the results and search strategy. It discusses each of the steps in a little more detail, with examples and suggestions on where to get help. This structured approach will help readers obtain a more focused set of results and, ultimately, save time and effort.

Keywords: Databases; Literature review; Literature search; Reference management software; Research questions; Search strategy.

• Databases, Bibliographic*
• Information Storage and Retrieval / methods*
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• Review Literature as Topic*

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ResearchGate

ResearchGate.  ResearchGate,  10115 Berlin, Germany;    http://www.researchgate.net ; free. 

Contemporary scholarly scientific research and publishing are characterized by a large number of journals, the fast tempo of publication, and the competitiveness of the funding process. These factors, in conjunction with the pervasive adoption of communication via social media platforms in academia, have given rise to a demand for new venues for scholars and scientists to collaborate on, publicize, share, and quantify the impact of their published works. Because medical librarians are an integral part of the research and scholarly communication process, the popularity of these new platforms calls for a basic familiarity with their features that an informed library professional can provide.

One example of a platform that has emerged in recent years in response to this demand is ResearchGate, a for-profit, social media–like scientific networking and collaboration website. The umbrella term “scholarly collaboration network” has been used to describe platforms like ResearchGate and its competitors.

ResearchGate was founded in Berlin in 2008 by two physicians and a computer scientist. Since its debut, the site has successfully attracted both large numbers of users as well as substantial private investment [ 1 ]. ResearchGate claims to have reached the 15 million member mark in 2017 [ 2 ].

ResearchGate’s primary feature is the individual researcher profile, which is used to promote scholarly production. The site creates profiles with information harvested from literature databases and other sources, while permitting researchers to create profiles by registering on the site. Standard elements of a profile include a dashboard-like overview, citations to published work, contact and career information, research interests, links to citations of potential interest, and selected impact metrics. Profiles can be augmented by including contact information, a photograph, citations to work that has not been discovered by ResearchGate, and full-text article content for sharing with other members. Site members can follow other researchers and their work, identify colleagues and coworkers such as lab personnel, and share details of current projects.

One distinctive feature is a question submission-and-response knowledgebase, allowing members to pose, respond to, and track questions regarding research and other topics of interest. There is also a proprietary quantitative altmetric called RG score. This score is based on work appearing in the researcher profile and other ResearchGate members’ interactions with it. The RG score has attracted criticism aimed at a lack of transparency in how it is calculated and at vulnerabilities leading to the potential of intentional inflation by those seeking to abuse it [ 3 ].

Revenue streams for the website include advertising that appears on its question-and-answer database page, job recruitment listings, and conference announcements. These displays are customized for individual users.

One characteristic ResearchGate shares with social media platforms is vigorous user engagement activity. The site frequently generates emails encouraging members to log in to monitor how many new views their profiles have garnered, how many members are following their research, and other metrics of engagement. While such notification messages can be managed in member account settings, these persistent enticements to spend time on the site mimic aspects of social media and are drawing increasing amounts of criticism. As with other social media platforms, the potential for misuse and malicious exposure of the accumulated user data are also concerns.

The high visibility that ResearchGate has achieved has not come without controversy. During the platform’s rise to prominence, one factor in its popularity was the large volume of full-text portable document format (PDF) articles present in many researcher profiles. These full-text PDFs were easily discoverable in web searches, making ResearchGate a popular source for article sharing.

Relevant to note is that a sizeable percentage of the articles that were available on ResearchGate were versions of PDFs that were protected by copyright law and not permitted to be shared. This fact came to the attention of a number of publishers and resulted in a coordinated effort on their part to address this issue.

In 2017, a group of publishers, including such large firms as ACS Publications and Elsevier, formed an organization called the Coalition for Responsible Sharing to pressure ResearchGate to take measures against distributing copyright-protected material on its platform. The coalition advocated for adherence to the International Association of Scientific, Technical and Medical (STM) Publishers’ “Voluntary Principles for Article Sharing on Scholarly Collaborations Networks,” a document outlining parameters for approved sharing among researchers [ 4 ].

ResearchGate responded to this pressure by removing some copyright-protected content, but at the time of this writing, the issue had not been completely resolved. Several large publishers, including SpringerNature, have recently announced an agreement to explore ways to allow their content to be shared legally on ResearchGate [ 5 ]. ACS and Elsevier are pursuing the matter in a US federal court [ 6 ].

ResearchGate’s success in building a large user base gives it the potential to survive the substantial legal challenges it faces. While the platform’s scale and attractive user interface may appeal to many researchers, issues such as a lack of transparency in the composition of the RG score, concerns regarding use of member data, and an attitude of ambivalence toward the complicated topic of article sharing contribute to a strong case that ResearchGate is not the optimal solution to the pressing need for a space for scholars and scientists to freely collaborate and communicate regarding their work.

Copyright compliance and infringement in ResearchGate full-text journal articles

• Published: 16 February 2017
• Volume 112 , pages 241–254, ( 2017 )

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• Hamid R. Jamali   ORCID: orcid.org/0000-0003-1232-6473 1  

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ResearchGate is increasingly used by scholars to upload the full-text of their articles and make them freely available for everyone. This study aims to investigate the extent to which ResearchGate members as authors of journal articles comply with publishers’ copyright policies when they self-archive full-text of their articles on ResearchGate. A random sample of 500 English journal articles available as full-text on ResearchGate were investigated. 108 articles (21.6%) were open access (OA) published in OA journals or hybrid journals. Of the remaining 392 articles, 61 (15.6%) were preprint, 24 (6.1%) were post-print and 307 (78.3%) were published (publisher) PDF. The key finding was that 201 (51.3%) out of 392 non-OA articles infringed the copyright and were non-compliant with publishers’ policy. While 88.3% of journals allowed some form of self-archiving (SHERPA/RoMEO green, blue or yellow journals), the majority of non-compliant cases (97.5%) occurred when authors self-archived publishers’ PDF files (final published version). This indicates that authors infringe copyright most of the time not because they are not allowed to self-archive, but because they use the wrong version, which might imply their lack of understanding of copyright policies and/or complexity and diversity of policies.

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Acknowledgement

The author would like to thank Mr. M. Sangari for his help in part of the data collection.

The study was partially funded by Kharazmi University (Iran).

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The Scholarly Kitchen

What’s Hot and Cooking In Scholarly Publishing

The Latest “Crisis” — Is the Research Literature Overrun with ChatGPT- and LLM-generated Articles?

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Elsevier has been under the spotlight this month for publishing a paper that contains a clearly ChatGPT-written portion of its introduction. The first sentence of the paper’s Introduction reads, “Certainly, here is a possible introduction for your topic:…” To date, the article remains unchanged, and unretracted. A second paper , containing the phrase “I’m very sorry, but I don’t have access to real-time information or patient-specific data, as I am an AI language model” was subsequently found , and similarly remains unchanged. This has led to a spate of amateur bibliometricians scanning the literature for similar common AI-generated phrases, with some alarming results . But it’s worth digging a little deeper into these results to get a sense of whether this is indeed a widespread problem, and where such papers have made it through to publication, where the errors are occurring.

Several of the investigations into AI-pollution of the literature that I’ve seen employ Google Scholar for data collection (the link above, and another here ). But when you start looking at the Google Scholar search results, you notice that a lot of what’s listed, at least on the first few pages, are either preprints, items on ResearchGate, book chapters, or often something posted to a website you’ve never heard of with a Russian domain URL. The problem here is that Google Scholar is deliberately a largely non-gated index. It scans the internet for things that look like research papers (does it have an Abstract, does it have References), rather than limiting results to a carefully curated list of reputable publications. Basically, it grabs anything that looks “scholarly”. This is a feature, not a bug, and one of the important values that Google Scholar offers is that it can reach beyond the more limiting inclusion criteria (and often English language and Global North biased) content of indexes like the Web of Science.

But what happens when one does similar searches on a more curated database, one that is indeed limited to what most would consider a more accurate picture of the reputable scholarly literature? Here I’ve chosen Dimensions , an inter-linked research information system provided by Digital Science, as its content inclusion is broader than the Web of Science, but not as unlimited as Google Scholar. With the caveat that all bibliometrics indexes are lagging, and take some time to bring in the most recently published articles (the two Elsevier papers mentioned above are dated as being from March and June of 2024 and so aren’t yet indexed as far as I can tell), my results are perhaps less worrying. All searches below were limited to research articles (no preprints, book chapters, or meeting abstracts) published after November 2022, when ChatGPT was publicly released.

A search for “Certainly, here is” brings up a total of ten articles published over that time period. Of those ten articles, eight are about ChatGPT, so the inclusion of the phrase is likely not suspect. A search for “as of my last knowledge update” gives a total of six articles, again with four of those articles focused on ChatGPT itself. A search for “I don’t have access to real-time data” brings up only three articles, all of which cover ChatGPT or AI. During this same period, Dimensions lists nearly 5.7M research articles and review articles published, putting the error rate for these three phrases to slip through into publications at 0.00007%.

Retraction Watch has a larger list of 77 items (as of this writing), using a more comprehensive set of criteria to spot problematic, likely AI-generated text which includes journal articles from Elsevier, Springer Nature, MDPI, PLOS, Frontiers, Wiley, IEEE, and Sage. Again, this list needs further sorting, as it also includes some five book chapters, eleven preprints, and at least sixteen conference proceedings pieces. Removing these 32 items from the list suggests a failure rate of 0.00056%.

While many would argue that this does not constitute a “crisis”, it is likely that such errors will continue to rise, and frankly, there’s not really any excuse for allowing even a single paper with such an obvious tell to make it through to publication. While this has led many to question the peer review process at the journals where these failures occurred, it’s worth considering other points in the publication workflow where such errors might happen. As Lisa Hinchliffe recently pointed out , it’s possible these sections are being added at the revision stage or even post-acceptance. Peer reviewers and editors looking at a revision may only be looking at the specific sections where they requested changes, and may miss other additions an author has put into the new version of the article. Angela Cochran wrote about how this has been exploited by unscrupulous authors adding in hundreds of citations in order to juice their own metrics. Also possible, the LLM-generated language may have been added at the pageproof stage (whether deliberately or not). Most journals outsource typesetting to third party vendors, and how carefully a journal scrutinizes the final, typeset version of the paper varies widely. As always, time spent by human editorial staff is the most expensive part of the publishing process, so many journals assume their vendors have done their jobs, and don’t go over each paper with a fine toothed comb unless a problem is raised.

Two other important conclusions can be drawn from this uproar. The first is that despite preprints having been around for decades, those both within and adjacent to the research community clearly do not understand their nature and why they’re different from the peer reviewed literature, so more educational effort is needed. It should not be surprising to anyone that there are a lot of rough early drafts of papers or unpublishable manuscripts in SSRN (founded in 1994) or arXiv (launched in 1991). We’ve heard a lot of concern about journalists not being able to recognize that preprints aren’t peer reviewed, but maybe there’s as big a problem much closer to home. The second conclusion is that there seems to be a perception that appearing in Google Scholar search results offers some assurance of credibility or validation. This is absolutely not the case, and perhaps the fault here lies with t he lack of differentiation between the profile service offered by Google Scholar , which is personally curated by individuals and its search results which are far less discriminating.

Going forward, I would hope that at the journals where the small number of papers have slipped through, an audit is underway to better understand where the language was introduced and how it managed to get all the way to publication. Automated checks should be able to weed out common AI language like this, but they likely need to be run at multiple points in the publication process, rather than just on initial submissions. While the systems in place seem to be performing pretty well overall, there’s no room for complacency, and research integrity vigilance will only become more and more demanding.

David Crotty

David Crotty is a Senior Consultant at Clarke & Esposito, a boutique management consulting firm focused on strategic issues related to professional and academic publishing and information services. Previously, David was the Editorial Director, Journals Policy for Oxford University Press. He oversaw journal policy across OUP’s journals program, drove technological innovation, and served as an information officer. David acquired and managed a suite of research society-owned journals with OUP, and before that was the Executive Editor for Cold Spring Harbor Laboratory Press, where he created and edited new science books and journals, along with serving as a journal Editor-in-Chief. He has served on the Board of Directors for the STM Association, the Society for Scholarly Publishing and CHOR, Inc., as well as The AAP-PSP Executive Council. David received his PhD in Genetics from Columbia University and did developmental neuroscience research at Caltech before moving from the bench to publishing.

11 Thoughts on "The Latest “Crisis” — Is the Research Literature Overrun with ChatGPT- and LLM-generated Articles?"

There is a huge difference between an entire article being written by AI, as the title of this post suggests, and having a few paragraphs being written by AI, as the actual evidence in most of this post suggests. I think the most worrisome part of this is that the journal editors are doing such a poor job that they aren’t catching those obvious “certainly…” type phrases, not because of what they imply about authorship, but just because they don’t belong in the final text at all.

I find the debate about using AI and academic integrity is bringing out an inconsistency in our very reasons for opposing “plagiarism”. That is, is the problem that the ideas aren’t yours per se, or that you are “stealing” another person’s ideas? When the “other person” is not a human, suddenly this distinction is in sharp relief. If you aren’t stealing from someone else, is there still a bad thing happening here, or is this just a much more “humanities” version of using R or SPSS to do your quantitative analysis? Usually I find it frustrating when people mix up copyright law with plagiarism, but in this situation I think copyright law has something useful to inform the plagiarism discussion. Copyright law in the US at least is very clear that non-humans (eg monkeys, elephants, and computers) can’t be credited with authorship/creatorship.

• By Melissa Belvadi
• Mar 20, 2024, 7:59 AM
• Reply to Comment

“Also possible, the LLM-generated language may have been added at the pageproof stage…”. That seems a bit far fetched. More likely some manuscripts don’t get carefully reviewed. More interesting to me than including LLM text in low quality articles is the sophistication of AI generated cell biology images. They’re getting to the point that sleuths like Elisabeth Bik can’t tell them from the real thing.

• By Chris Mebane
• Mar 20, 2024, 8:55 AM

The authors of one of the suspect papers have stated that the inclusion of the text was a cut-and-paste error. Why would such an error be “far fetched” during corrections but less so in other parts of the publication process?

• By David Crotty
• Mar 20, 2024, 9:27 AM

This is an interesting discussion; thanks for advancing it. Given the challenges facing scientific and medical publishing, which is a bigger crisis? 1) That overworked, underpaid, stressed, researchers and academics who don’t directly benefit financially from their content are now leveraging AI to speed time to publication, or 2) That the majority of entrenched publishers continue to act as if human friction and delays in getting novel science and groundbreaking medicine to patients and doctors who are trying to improve human suffering, which could be improved significantly with AI, is itself a problem to be dealt with? Full disclosure, I serve as Chief AI Officer of Inizio Medical and am a Founding Board Member of the Society for Artificial Intelligence and Health, but my comment is mine and mine alone.

• By Matt Lewis
• Mar 20, 2024, 9:52 AM

It is alarming that this slipped through after all the work done to tighten up publishing integrity over the last year or two. It suggests more sophisticated fake/fraudulent AI content is also getting through. More generally, it is another embarrassing, public black eye for scholarly publishing and science.

• By Curtis Brundy
• Mar 20, 2024, 10:42 AM

It’s clear from this post that the crisis is not about generative AI, which simply reveals the underlying problem. The real issue is the faulty editorial workflow, which Angela Cochran identified as far back as 2017. The human editor should have the last check of the paper. If you allow authors to make changes, then those changes should be monitored by the editor, and a system such as track changes applied to ensure that any changes the author makes, whether or not requested by the editor, are identifiable for checking.

What this reveals is that scholarly publishing still retains a culture of trust, in this case, trust that the author will not make extensive changes at proof stage. It is no longer possible to run scholarly publishing with the assumption that the author will behave responsibly.

• By Michael Upshall
• Mar 20, 2024, 10:57 AM

I think you’re largely right, but those needs are directly in opposition to the increasing pressure for journals to 1) publish faster, and 2) publish cheaper. As noted in the post, the sorts of human interventions you suggest are the most expensive parts of the process. Would the community accept slower publication and higher APCs/subscription prices in order to ensure this level of scrutiny?

• Mar 20, 2024, 11:00 AM

Well, that’s for the scholarly community to decide, but personally, I don’t think there is a choice, to preserve the credibility of academic publishing.

• Mar 20, 2024, 11:10 AM

What Automated Checks systems do you recommend be used by journals?

• By Deb Whippen
• Mar 20, 2024, 11:24 AM

“Automated” is a loaded word here — every check that I know of requires at least some level of human interpretation and intervention.

That said, there are good recommendations coming out of the STM Research Integrity Hub ( https://www.stm-assoc.org/stm-integrity-hub/ ) and tools include plagiarism checkers, image integrity checkers, and paper mill checkers. As far as I know, there are no reliable automated tools for determining whether text or images were AI-generated. But screening for phrases like the ones mentioned above seems a reasonable step.

And as noted, this makes publishing slower and more expensive.

• Mar 20, 2024, 11:47 AM

As the EIC of a journal, I read every manuscript that is submitted. Lately, I have detected a handful of manuscripts that have the hallmarks of being generated (all or in part) by an LLM. It is fairly obvious (to me) to identify such manuscripts, because the text reads like “word salad”, that is, bland, general sentences that do not seem to converge on a clear meaning and lack specific details. A superficial reading is not good enough in such cases. As pointed out in the post, having actual humans read the manuscripts or proofs is necessary, but expensive.

• By Constance Senior
• Mar 20, 2024, 2:15 PM

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