The Presence and Nature of AI-Use Disclosure Statements in Medical Education Journals: A bibliometric study

The Presence and Nature of AI-Use Disclosure Statements in Medical Education Journals: A bibliometric study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The Presence and Nature of AI-Use Disclosure Statements in Medical Education Journals: A bibliometric study M. Ans , View ORCID Profile L. Maggio , H. Algodi , J. Costello , View ORCID Profile E. Driessen , View ORCID Profile K. Oswald , View ORCID Profile L. Lingard doi: https://doi.org/10.1101/2025.11.11.25340015 M. Ans 1 Schulich School of Medicine and Dentistry, Western University Find this author on Google Scholar Find this author on PubMed Search for this author on this site L. Maggio 2 Department of Medical Education, University of Illinois , Chicago Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for L. Maggio H. Algodi 1 Schulich School of Medicine and Dentistry, Western University Find this author on Google Scholar Find this author on PubMed Search for this author on this site J. Costello 2 Department of Medical Education, University of Illinois , Chicago Find this author on Google Scholar Find this author on PubMed Search for this author on this site E. Driessen 3 Faculty of Health, Medicine and Life Sciences, Maastricht University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for E. Driessen K. Oswald 4 Faculty of Information and Media Studies, Western Universtiy Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for K. Oswald L. Lingard 5 Department of Medicine and Centre for Education Research & Innovation, Schulich School of Medicine and Dentistry, Western University Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for L. Lingard For correspondence: lorelei.lingard{at}schulich.uwo.ca Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background As AI-use becomes more common in research, disclosure policies have emerged to ensure transparency and appropriateness. However, database research in other fields suggests that disclosure may lag behind AI-use. Medical education journal editors report that submitted manuscripts rarely include AI-use disclosures, and they perceive a lack of clarity regarding when and how AI-use should be disclosed. However, we lack objective evidence regarding the incidence and nature of AI-use disclosure in medical education. Methods Using bibliometric methods, we searched a database of 24 leading medical education journals for articles published between January and July 2025 (n=2,762 articles). Screening with Covidence software excluded 716 non-empirical and/or non-English language articles. The remainder (n=2,046) were examined for the presence of AI-use disclosures, which were content-analyzed. Results 2.5% of empirical articles (n=51) had an AI disclosure statement. BMC Medical Education contained the most disclosures (24), followed by Medical Teacher (7) and Journal of Surgical Education (4). Forty-two articles were authored in non-native English-speaking countries, and 69.4% of all first authors had begun publishing in the past decade. Disclosures averaged 43 words and described use superficially: most commonly “editing” and “translation”. Of 18 named tools, ChatGPT was most common. Most disclosures explicitly attested to author responsibility for AI-produced material. Disclosures usually appeared in acknowledgements; those located in methods lacked responsibility attestation. Negative disclosures attesting that AI was not used were also present. Discussion AI-use disclosures in medical education journals are rare and appear mostly in work from non-native English-speaking regions of the world. A shared disclosure practice is evident: name the tool and affirm author responsibility, but describe use superficially. This suggests a practice of “safe” disclosure that may be more performative than informative, therefore failing to satisfy the goal of ensuring transparent and ethical AI use in research. Background As AI-use becomes more common in research 1 , scientific communities rely on disclosure to ensure that AI is used ethically and appropriately. Trust in science depends on this 2 , which is why disclosure policies from organizations such as the International Committee of Medical Journal Editors 3 , publishers and journals 4 , 5 share an emphasis on transparency and accountability: authors are required to explicitly disclose whether they used AI-assisted technologies in the production of submitted work, and to take responsibility for all AI-produced material 6 , 7 . However, emerging research suggests that AI disclosure may be lagging behind AI use. A 2025 Nature survey of 5000 researchers found diverging views of disclosure 8 . For instance, when asked whether they had used AI to write a section of a paper and not disclosed the AI use, 17% of mid-career researchers said ‘Yes’, and 47% said ‘No, but I would be willing to’. Database research also suggests that AI-use outstrips disclosure. For instance, one comparative study of >5 million articles in the Dimensions database reported a 468% increase in clusters of ChatGPT-preferred positive terms (e.g., meticulous, intricate, commendable) in texts published in 2023 after the model became available; only .1% of those papers contained language suggesting disclosure 9 . Similarly, a 2024 analysis of abstracts published in high-impact orthopaedic journals found that of 28 containing AI-generated text, only 1 disclosed AI-use despite journal requirements 10 . AI-use is a hot topic in contemporary medical education conversations: international conferences are abuzz with keynotes, panels, workshops, and papers related to AI 11 . But while it is increasingly apparent that scholars are using AI 12 , the nature and pattern of our disclosure practices is less clear. In a recent interview study, medical education journal editors described infrequent experience with AI-use disclosures in submitted manuscripts 13 . Most suspected that medical education researchers might be using AI without disclosing, and worried that this may arise from a lack of clarity regarding when disclosure is necessary and what details are required. To deepen understanding of AI-use disclosure in medical education, we require objective evidence of researchers’ current disclosure practices. Therefore, this study asks: what is the rate and nature of AI-use disclosure in empirical research papers in medical education journals? Methods We conducted a bibliometric study to identify and characterize AI disclosure statements appearing in medical education research articles. Our sampling frame included all articles published between January 1 to June 30, 2025, in the 24 leading medical education journals listed in the Medical Education Journals List 14 . We excluded non-empirical publications (e.g., commentaries, letters, perspectives) both because we perceived the stakes to be higher for disclosing AI-use in research than in commentaries and we anticipated that the conventional genre of empirical articles would support meaningful comparisons regarding disclosure content and location. Screening and article selection were managed in Covidence , a web-based knowledge synthesis software. MA, HA, JC, and LM independently screened the titles and abstracts of all articles to exclude non-empirical publications. Full-texts of the remaining articles were then examined by MA and HA for AI-use disclosure statements. We classified articles as having an AI statement if they explicitly disclosed the use of an AI tool (e.g., ChatGPT) or described how AI was used in the preparation of the work. Articles that merely focused on, evaluated, or discussed an AI tool, without indicating that AI was used to generate or assist with the manuscript, were not counted as having an AI disclosure statement. For example, an article studying the effectiveness of AI in clinical reasoning, but lacking an explicit disclosure of AI involvement in the authorship process, would not be considered to have a statement. Articles with an AI-use disclosure statement proceeded to data extraction ( Figure 1 ). Download figure Open in new tab Figure 1. PRISMA Chart MA and HA each individually extracted data from approximately half of the articles using a structured Google Sheets template. They met regularly to ensure consistent interpretation discussing ambiguous or complex disclosure statements and reaching consensus through discussion. For each article with an AI-use disclosure statement, we extracted: Bibliographic variables: DOI, article title, journal name, publication date, journal impact factor, publisher. Author-level variables (first author only): name, total publications, total citations, H-index, year of first publication, institutional affiliation, career position, and country of affiliation. These data were collected using Web of Science (WoS). Although we recognize that authors may have publications not indexed in the WoS, we viewed this as a reasonable proxy measure for publishing experience. For bibliographic and author-level variables, in July 2025, we downloaded the metadata from WoS; if unavailable on WoS, we queried Scopus, except for author career position. Author career position was extracted from the article’s author information or if unavailable, from ResearchGate. Additionally, based on a review of journal and publisher websites, we identified if AI reporting guidance was provided. Disclosure-specific extractions focused on the name of AI tool(s) used, purpose of AI use, location of the disclosure statement (e.g., methods, acknowledgements, disclosures), verbatim text of the statement, statement word count, and the presence or absence of an attestation of responsibility (e.g., authors remain accountable for the content). We also extracted measures of transparency to situate AI disclosure statements within the broader context of established transparency practices. These included open access status, presence of a funding statement, conflict of interest disclosure, and data availability statement. For analysis, we conducted descriptive statistics to characterize the bibliographic and author-level variables. To describe the content of AI-use disclosure statements, we conducted a content analysis for AI tool used, the nature of usage, and any reference to responsibility attestation. Results Over the study period, 2,762 articles were published in the medical education journals sampled. Of these, we excluded 716 (25.9%) non-empirical articles. Of the remaining 2,046 articles, 2.5% (n = 51) had an AI disclosure statement. 15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66 Thirteen of the journals included articles with disclosures. Of these, 12 (92.3%) provided author guidance on disclosure on their website/author instructions, or on their publisher’s website ( Table 1 ). BMC Medical Education published nearly half the identified statements (n = 24, 47.1%), followed by Medical Teacher (n = 7, 13.7%) and the Journal of Surgical Education (n = 4, 7.8%). Nine journals did not include any articles with disclosures. Two journals ( African Journal of Health Professional Education and BMJ Stimulation & Technology Enhanced Learning ) did not publish any empirical research during the study period and, therefore, were not examined for AI-use disclosures. View this table: View inline View popup Table 1. Distribution of AI-Use Disclosures by Journal ** African Journal of Health Professional Education and BMJ Stimulation & Technology Enhanced Learning did not have any empirical studies published to analyze. Focus on Health Professional Education had the highest proportion of disclosure statements (25%), although this reflects a small denominator of total articles published, with 2 of 8 containing disclosure statements. Across the other 23 journals, the prevalence of AI-use disclosure statements was low, with no journal exceeding 7% of published articles ( Table 1 ). Author Characteristics Authors were affiliated with institutions across all six World Health Organization (WHO) regions ( Table 2 ) with 24 countries represented (Online Supplemental Appendix A). Fifty unique first authors from 46 institutions included disclosure statements in their articles. Authors affiliated with institutions in the Western-Pacific region accounted for the largest share of disclosures (n = 15, 29.4%), followed by Europe (n = 11, 21.6%) and the Americas (n = 11, 21.6%). Disclosures were also reported by authors from the Mediterranean (n = 9, 17.6%), Southeast Asia (n = 4, 7.8%), and Africa (n = 1, 2.0%). At the country level, USA, Germany, Iran and Australia were most represented. View this table: View inline View popup Download powerpoint Table 2. Regional Distribution (WHO Regions) of First Authors of Articles with AI-Use Disclosures (n = 51) On average, first authors had published a median of 11.5 publications (range 1–142, SD=34.7). The median h-index, a citation-based metric, for authors was 4 (range 0-35, SD=7.3) with the median author citations received being 55 (range 0–10,214, SD=2266.2). Thirty-three (66.0%) authors published their first article within the last decade, and for 3 (6.0%) authors, this was their first publication. Author career positions were variable. Among 51 first authors, there were 50 unique authors with the most common designation being Assistant Professor (n = 9, 17.6%), followed by: other (e.g. clinical roles or administrative positions) (n = 8, 15.7%), trainees or early career researchers (e.g. residents, PhD students, post-doctoral researchers) (n = 7, 13.7%), Associate Professor (n = 6, 11.8%), lecturers/senior lecturers (n = 6, 11.8%),research staff or fellows (n = 6, 11.8%), and Professor (n = 4, 7.8%). Four author career positions were unreported. AI tools and Nature of AI-Use Authors disclosed the use of 18 unique AI tools. ChatGPT was the most disclosed tool (n = 19, 37.3%), followed by Otter.ai (n = 11, 21.6%), and Editage (n = 3, 5.9%) ( Table 3 ). Five articles (9.8%) combined multiple tools, and 4 (7.8%) disclosed AI use without naming a specific tool. Four articles (7.8%) explicitly included negative disclosure statements, stating that no AI tools were used such as: “ Generative AI was not used for any aspect of this study including drafting of the manuscript .” 59 The 4 negative disclosures originated from 2 authors in China, 1 in Hungary, and 1 in Qatar 46 , 47 , 59 , 62 . View this table: View inline View popup Download powerpoint Table 3. AI Tools Reported in Disclosure Statements (n = 51) *Some articles reported multiple tools hence the total is greater than 51. We characterized the use of AI into six types, based on the language in the disclosures ( Table 4 ). These categories included: editing, transcription, thematic/data analysis, drafting and article screening. Use of AI for editing purposes was the most reported (n = 28, 54.9%) followed by transcription (n = 12, 23.5%), thematic/data analysis (n = 7, 9.8%), drafting (n = 3, 5.9%), and article screening in knowledge syntheses (n = 2, 3.9%). View this table: View inline View popup Download powerpoint Table 4. Nature of AI Use in Disclosure Statements (n = 51) ** some articles cited multiple uses of AI hence the total being greater than 49. AI was most frequently described as being used for language-related functions. Editing was cited in 28 (54.9%) articles, with representative phrasings such as “ we would like to thank Editage for English language editing ” 40 or “ the authors used ChatGPT to improve language and readability .” 42 Transcription was described in 12 (23.5%) articles: e.g., “ the interview data was transcribed using Otter . ai and then manually checked for accuracy .” 63 Eight unique articles (15.7%) disclosed more substantive AI uses. For example, 4 articles (7.8%) disclosed use of thematic analysis (e.g., one group of “ authors acknowledge the use of Claude 3 . 5 Sonnett for thematic analysis of qualitative data ”). 15 AI use for data analysis was reported in 4 (7.8%) articles, described in one study as “ ChatGPT was used to assist in identifying descriptive trends and generating narrative summaries . “ 65 Two articles (3.9%) reported AI for screening purposes, with one article saying, “ We used an open-source artificial intelligence (AI) tool, ASReview (V . 1 . 4)…ASReview employs a machine learning algorithm that prioritizes articles based on their textual proximity to previously identified relevant articles (by the researchers) .” 21 Disclosure statements were typically concise, a single sentence averaging 43 words (Range 11-200, SD 36.1). For instance, one stated that “ DeeplL AI writing assistant was used to enhance this manuscript for methodology, clarity, and style ” 17 , while another reported that “ Chat-GPT 3 was used to improve the clarity of the manuscript and for language editing ” 38 . As these representative examples illustrate, disclosures tended to include general terms such as “enhance” or “improve” without detailing the particular characteristics that were enhanced or how the AI was used to achieve such enhancement. In contrast, only 2 articles (3.9%) had a disclosure word count of over 100 words and offered more precise indications of the nature of AI use. For example, one disclosure stated: “ The questionnaire for this study was designed with the assistance of AI tools, demonstrating AI’s potential as a research collaborator. The collected data were initially explored using ChatGPT, an AI-powered language model, to assist in identifying descriptive trends and generating narrative summaries. ChatGPT was not used for statistical calculations or hypothesis testing. Artificial Intelligence tool, specifically OpenAI’s ChatGPT (version GPT-4, accessed via ChatGPT Plus) we used at various stages of this research. The following contributions were made: Questionnaire Development: ChatGPT was used to draft survey items aligned with the research objectives. Prompts included, for example: “Design a student survey to evaluate the use of AI tools in medical education across academic, clinical, and research contexts . ” Thematic Analysis Assistance: For qualitative responses, ChatGPT helped group answers into initial themes. These were reviewed, corrected, and finalized by the authors to ensure accuracy and context. Narrative Drafting: ChatGPT was used to generate narrative summaries of the findings and to draft portions of the introduction, results, and discussion. Prompts included: “Summarize Likert-scale findings and interpret trends,” and “Rewrite this paragraph in an academic tone . ” Language Polishing: ChatGPT helped improve the clarity, grammar, and coherence of the manuscript . ” 65 Only 29% (n=15) of disclosures included an attestation of responsibility. For example, authors wrote “ After using this tool, the authors reviewed and edited the content as needed and took full responsibility for the content of the publication . ” 25 Location of disclosure statements Placement of disclosure statements varied. Approximately one-third of the disclosures (n = 19, 37.3%) appeared in the acknowledgements section. Another 15 (29.4%) articles appeared in a dedicated AI-use disclosure section, which consistently appeared at the end of the published manuscript, grouped with other declarations such as conflict of interest or data availability statements. For the remaining statements, 14 (27.5%) articles exclusively reported their disclosure in the methods, 2 (%) in both the methods and a dedicated AI disclosure section, and 1 (%) in the discussion. We noted a relationship between location and content of disclosures: those located in the methods primarily described AI use for transcription, e.g., “ All the audio-recorded interviews were transcribed verbatim into Microsoft Office using Otter . ai . ” 39 Additionally, attestation statements were more likely to be included in disclosures placed in the acknowledgements or a dedicated AI use disclosure section such as: “ During the preparation of this manuscript, the authors utilized ChatGPT to help refine the language. After using this tool, the authors reviewed and made any necessary adjustments and took full responsibility for the content of the publication .” 59 By contrast, these attestation statements were absent when the disclosures appeared in the methods. Transparency metrics We found that 42 articles (82.4%) were published as open-access, 42 articles (82.4%) included a funding statement, 40 articles (78.4%) had a conflict-of-interest statement, and 37 articles (72.5%) had a data availability statement. In most cases, AI-use disclosures appeared directly alongside these established measures of academic transparency (n = 34, 66.7%) at the end of the publication. Discussion Our findings highlight three primary issues for consideration in our field: the low rate of disclosure, the geographical pattern of disclosures concentrated in non-native English-speaking regions, and the practice of superficial, safe disclosures. Our analysis suggests that AI-use disclosure is rare in medical education: only 2.5% of empirical articles published in 24 medical education journals in the first half of 2025 included a disclosure. This corroborates medical education journal editors’ subjective experience of infrequently seeing disclosures in submitted manuscripts 67 . It also resembles bibliometric results from other research domains: e.g., an analysis of academic radiology articles in 2024 found that 34 of 1998 manuscripts (1.7%) disclosed AI use 68 . Does this low rate signal low AI-use, or a tendency towards nondisclosure of AI-use? Unfortunately, evidence leans towards the latter 69 . For instance, since 2023, analyses by “integrity specialists” have flagged hundreds of published research papers with obvious signs of undisclosed AI use, such as those that contain the phrase “regenerate response” 70 . Given that the field of medical education research is not immune to undisclosed AI-use 71 , we need to explore the underpinning influences. One influence is the lack of clarity. Not only are journal policies on AI-use and disclosure shifting quickly due to the fast-changing AI landscape, journals’ positions vary. For authors submitting, and resubmitting, their manuscripts, disclosure requirements may appear murky and complicated. Another influence is the divergent attitudes among researchers about AI-use and disclosure: surveys have clearly shown that we don’t agree about what AI should be used for and when those uses require disclosure 8 , 12 . A third influence is psychological/cultural and institutional barriers that make researchers hesitant to disclose AI-use to improve their writing. They may experience a sense of “demonization” of AI 72 , inducing guilt or shame about using it to support their work 73 and fear of consequences such as academic stigma and evaluation bias 74 . Finally, the lack of published disclosures may act like a self-fulfilling prophecy: if we suspect others are using AI but we rarely see AI-use disclosures, that can send a tacit message that disclosure is unnecessary. Our findings also demonstrate a particular geographical pattern to AI-use disclosure in the analyzed articles, with most authored by individuals from non-native English-speaking regions of the world. This likely explains the dominant use of AI tools for language-related tasks, like editing, translating and grammar checking, all of which are valuable to authors writing in English as a non-native language, and resonates with other studies that have reported higher AI-use for writing support by non-native English researchers 75 . Given this geographical pattern of disclosure, medical education needs to be aware of the possibility of stigmatization, given that non-native English academic writing is more likely to raise reviewers’ suspicions about AI-use 76 , and is also known to be prone to misclassification by GPT detectors 77 . The four negative disclosures in our dataset all came from authors in non-native English-speaking regions, which may suggest an attempt to deflect such potential stigmatization. Medical educators need to be aware that, rather than levelling the global playing field in academic publishing, AI-use disclosure might intensify inequity and bias against authors from some parts of the world 78 , further exacerbating the global north dominance in the field’s literature 79 , 80 . The disclosures we analyzed were almost uniformly brief, describing general uses such as “editing”, and lacking in detail that would allow a reader to understand precisely how the author engaged with the AI, iteratively refined and structured its work, and verified its outputs. We interpret this as “safe” disclosure practice: authors are mostly including the required elements of tool, task, and attestation, but rarely disclosing substantive, intellectual tasks, or elaborating the ‘how’ of their interaction with the AI. On the one hand, this might be good news: the disclosed uses we found mostly map onto the “acceptable” uses outlined in a recent framework distinguishing ethically acceptable, contingent and suspect uses of AI 81 . On the other hand, though, we worry that our dataset includes few disclosures in the framework’s “contingent” or “suspect” ranges not because researchers are not using AI these ways, but because they perceive themselves to be on shaky ethical ground when they do. While such frameworks can be powerful aids to scholarly conversation about AI use and disclosure, we must take care that they do not unintentionally drive a tacit culture of nondisclosure. The combination of low disclosure rates and safe disclosure practice produces a “transparency paradox” 82 : mandatory disclosure that does not attend to social complexities leads to both nondisclosed AI-use and disclosure “theatre” in which published disclosures are more performative than informative 83 . One possible solution is for journals to create dropdown menus of possible AI uses and verification strategies, signaling sanctioned uses and expected levels of detail. These menus might draw from emerging disclosure frameworks specifying tiers of usage 84 or using CREDiT-like author contribution frameworks 85 . Another solution might be to shift away from ‘disclosure’ language altogether and make AI use a routine part of methods reporting. This could help to normalize writing about AI use in manuscripts, thus reducing the negative valence associated with disclosure in the current literature. Given our results, however, we anticipate that authors will need explicit guidance to include responsibility attestations when reporting AI-use within their Methods. Limitations This study has several limitations. First, our analysis was limited to the first 6 months of 2025. Because editors have reported a recent increase in AI-related disclosures 67 , it is possible that disclosure rates were higher during this period, but we lack data to examine changes or trends over time. Moreover, it is possible that the low frequency of disclosures we observed may reflect the field’s publication timelines, given an average lag of approximately 188 days from submission to publication 86 , suggesting that the manuscripts referenced by editors may not yet have been published. Second, we did not compare articles with AI disclosures to those without, which limits our ability to assess differences in overall transparency practices. Finally, because many journals impose word limits, authors may have a limited word count to include and/or elaborate on their AI use, potentially constraining the detail and desire to include a disclosure statement. Conclusion AI-use disclosures in medical education journals are rare and appear mostly in work from non-native English-speaking regions of the world. A shared disclosure practice is evident: name the tool and affirm author responsibility, but describe use superficially. This suggests a practice of safe disclosure that may fail to satisfy the goal of ensuring transparent and ethical AI use in research. Data Availability All data produced in the present work are contained in the manuscript. Ethics Approval As a bibliometric study using published documents and no human subjects, this project did not require REB approval. Acknowledgements This project was funded by the Office of Continuing Professional Development, Schulich School of Medicine & Dentistry, Western University in the form of a 2024 Digital Research & Innovation Grant, as well as by a Summer Research Training Program grant to support the activities of Muhammad Ans. References 1. ↵ Weixin Liang , Yaohui Zhang , Zhengxuan Wu , et al. 2024 . 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