bims-skolko Biomed News
on Scholarly communication
Issue of 2026–08–02
thirty-six papers selected by
Thomas Krichel, Open Library Society
  1. Papers with open peer-review reports are less likely to be retracted.
  2. The Art of Storytelling in Scientific Writing.
  3. Predatory Publications Bias the Findings of Orthodontic Meta-analyses.
  4. Analysis of Retracted Publications in the Diabetes Literature.
  5. Scientists using LLMs will 'do more, less well', modelling study predicts.
  6. Toward Structured Transparency: A Governance Framework for AI Use in Biomedical Publishing.
  7. Authorship, Disclosures, and Conflicts of Interest in Papers Related to the Use of Artificial Intelligence.
  8. Large Language Models and the Future of Peer-Reviewed Medical Publishing: Challenges, Editorial Responses, and a Framework for Responsible Integration.
  9. Prevalence and Content of Generative Artificial Intelligence Use Policies in Iranian Medical Science Journals.
  10. Proper Utilization of Generative AI in Scholarly Writing: The Researcher and Author Perspective.
  11. Exploring the use of generative AI in the peer review process: effectiveness, verification and confidentiality.
  12. Scientific Publishing in the era of Artificial Intelligence: Integrity and Responsibility.
  13. What does generative AI mean for the future of nicotine and tobacco scientific publishing?
  14. AI in transplantation: levelling the playing field and raising the methodological standards.
  15. Comparing Human and AI-Generated Editorials in Orthopaedic Oncology: A Blinded Evaluation of Quality and Authorship Perception.
  16. Pitfalls in the Use of Artificial Intelligence in Dermatology Publishing: Important Considerations for Authors.
  17. [The benefits and limitations of artificial intelligence in scientific writing: beware of fraud].
  18. At the heart of peer review: why artificial intelligence concordance misses the human pulse.
  19. Comprehensive Consideration of Ethics in AI-assisted Scientific Writing and Peer Review.
  20. Why serve as a reviewer for an orthopaedic journal?
  21. The declaration of Adelaide white paper: guidelines for author responsibility in peer review.
  22. Who Funds Open Data Sharing? Analysis of data availability statements in biomedical publications.
  23. From data sharing to knowledge co-creation and evidence-based practice: Advancing military and Veteran health research.
  24. How to be seen: optimizing your manuscript for AI search.
  25. Large language models in academic writing: when Letters to the Editor stop serving scientific discourse.
  26. Temporal linguistic shifts in oncology randomized controlled trials following large language model availability: A corpus analysis of 21,392 publications.
  27. Writing for publication helps reduce the theory-practice gap and all nurses have a role to play.
  28. Journal response time: an experience at Pan African Medical Journal.
  29. The Diamond road to Open Science.
  30. Guidelines on the use of Artificial Intelligence for authors, reviewers, and editors of APJCN.
  31. The 39th Anniversary of the Medical Journal of the I.R of Iran.
  32. ASO Author Reflections: Artificial Intelligence in the Scientific Merit Review Process.
  33. Publication Fragmentation in Oral and Maxillofacial Surgery: Efficient Knowledge Dissemination or Ethical Redundancy?
  34. Auld Lang Syne: reflections and opportunities for engagement and growth on the 100th anniversary of Academic Medicine.
  35. Implementing a Department-wide Peer Learning Program Across a Large Academic Radiology Practice.
  36. RECOT looks to the future: internationalisation, excellence and scientific impact.
  1. Nature. 2026 Jul 29.
    Papers with open peer-review reports are less likely to be retracted.
    Dalmeet Singh Chawla.
      
    Keywords:  Peer review; Publishing
    DOI:  https://doi.org/10.1038/d41586-026-02282-1
  2. Inorg Chem. 2026 Jul 27. 65(29): 16625-16626
    The Art of Storytelling in Scientific Writing.
    Richard L Brutchey.
      
    DOI:  https://doi.org/10.1021/acs.inorgchem.6c03460
  3. J Dent Res. 2026 Jul 29. 220345261466209
    Predatory Publications Bias the Findings of Orthodontic Meta-analyses.
    M P Maroulakos, Z Tatas, D Al-Moghrabi, N Pandis, P S Fleming.
      This study quantified how frequently questionable publications by predatory or suspect journals appear in orthodontic evidence syntheses and examined whether inclusion of these studies influences pooled estimates in orthodontic systematic reviews (SRs) with meta-analyses (MAs). Orthodontic SRs with MAs published in English between January 1, 2020, and December 31, 2023, were identified through multiple databases. One MA per SR (prioritized primary outcome/earliest time point) was selected when data were extractable from forest plots. Journals were classified as legitimate, suspect, or predatory using a prespecified approach. For MAs containing at least 1 predatory or suspect study, pooled effect estimates were recalculated under 2 scenarios using random-effects models (restricted maximum likelihood), including 1) all studies and 2) legitimate studies only. Changes were assessed using differences in the pooled estimates between scenarios. From 10,905 records, 340 orthodontic SRs with accessible meta-analytical data were included, most commonly addressing treatment modalities (32.4%). Among the 1,992 primary studies identified, most were published in journals indexed in Medline (n = 1,494; 75%). Overall, 16.2% of SRs were published in predatory or suspect (questionable) journals. One-third of selected MAs (114/340; 33.5%) included at least 1 questionable primary study published in a predatory or suspect journal, and 21.4% (73/340) included at least 1 primary study published in a predatory journal. The inclusion of questionable primary studies was similar whether the SR was published in a legitimate (33.7%), suspect (30%), or predatory journal (33.3%). In the final reanalysis set (n = 106), the pooled estimate decreased in 55% and increased in 45% of MAs after removal of suspect/predatory studies. Predatory and suspect publications are present in 1 in 3 orthodontic meta-analyses and may influence the pooled estimates. Routine legitimacy screening, study quality assessment, and sensitivity analyses excluding questionable studies should be integrated into evidence synthesis and editorial assessment in orthodontics.
    Keywords:  decision-making; evidence-based dentistry; health care; meta-analysis; orthodontic(s); publishing
    DOI:  https://doi.org/10.1177/00220345261466209
  4. Med J Islam Repub Iran. 2026 ;40 20
    Analysis of Retracted Publications in the Diabetes Literature.
    Somayeh Parvin, Mahshid Lotfi, Mohsen Nouri.
       Background: Retraction serves as a critical corrective mechanism in scientific publishing, preventing the spread of flawed or misleading data. This study aimed to systematically analyze the characteristics of retracted articles within the field of diabetes research, highlight potential vulnerabilities in the integrity of diabetes research, and address gaps in understanding the nature and implications of research misconduct in this crucial medical discipline.
    Methods: This study aimed to analyze retracted publications in the field of diabetes published from 1978 to 2024 using the Web of Science database. Information from the selected articles was obtained through the Retraction Watch Database. Extracted details included the journal title, type of article, country of origin, publication and retraction dates, number of authors, and reasons for retraction. The collected data were analyzed using descriptive statistical methods.
    Results: Between 1978 and 2024, our review revealed 316 retracted articles. The journals Science (n = 1517) and The Lancet (n = 742) accounted for the highest number of these highly cited retractions, while the journals Biomed Research International (n = 31) and Diabetes (n = 15) accounted for the most cited of these retractions. The most common reasons for retraction were investigations initiated by the journal or publisher and concerns related to data integrity. The years with the highest number of retractions were 2023 (n = 91) and 2016 (n = 40), whereas the majority of retracted articles were initially published in 2022 and 2021 (n = 26). Collaborative research involving authors from China (n = 137), the United States (n = 71), and India (n = 24) was most frequently associated with these retractions.
    Conclusion: Safeguarding the integrity of scientific literature, particularly in high-stakes fields such as diabetes research, requires a fundamental shift from reactive retractions to multilateral proactive systemic reform. Cultivating an ethical research culture (changing incentives to reward research quality and transparency over quantity by Institutions and funders); empowering researchers (providing the skills for rigorous and ethical research through mandatory, ongoing ethics training); and ensuring vigilant publishing (enforcing clear policies, rapidly investigating issues, and performing transparent retractions to protect the scientific record by journals).
    Keywords:  Article; Duplication; Ethics; Plagiarism; Publication; Retraction
    DOI:  https://doi.org/10.47176/mjiri.40.20
  5. Nature. 2026 Jul 31.
    Scientists using LLMs will 'do more, less well', modelling study predicts.
    Kaia Glickman.
      
    Keywords:  Computer science; Machine learning; Publishing
    DOI:  https://doi.org/10.1038/d41586-026-02397-5
  6. J Korean Med Sci. 2026 Jul 27. 41(29): e318
    Toward Structured Transparency: A Governance Framework for AI Use in Biomedical Publishing.
    Jong-Min Kim.
      The rapid adoption of large language models and generative artificial intelligence (AI) is transforming biomedical research and publishing. Although international organizations such as the International Committee of Medical Journal Editors (ICMJE) and the Committee on Publication Ethics (COPE) have established the principle that AI cannot be recognized as an author, and the ICMJE, in its January 2026 revision, has introduced a dedicated section addressing AI use by authors, peer reviewers, and editors, journal-level Instructions for Authors still require further operational detail to apply these principles consistently throughout the publication process. This review critically examines current AI-related policies in the Journal of Korean Medical Science (JKMS) and the Korean Association of Medical Journal Editors (KAMJE) and identifies three major challenges: the practical limitations of the AI non-authorship principle, the inadequacy of current AI disclosure practices, and the emergence of AI-specific conflicts of interest that extends beyond conventional financial disclosures. We argue that medical publishing should move from a restrictive approach toward a framework of structured transparency that systematically documents, evaluates, and verifies AI use. To achieve this goal, we propose a practical governance framework that includes a three-tiered AI disclosure system, strengthened accountability for corresponding authors, expanded institutional conflict-of-interest disclosures, transparent reporting of AI use by peer reviewers, and formal editorial policies governing AI-assisted editorial activities. Future revisions of the KAMJE and JKMS Instructions for Authors should prioritize transparent and accountable AI governance rather than restricting AI use. Adoption of a structured, publication-wide framework encompassing authors, peer reviewers, and editors would strengthen research integrity while supporting the responsible integration of AI into biomedical publishing.
    Keywords:  Generative Artificial Intelligence; Guidelines; Large Language Models; Medical Publishing; Peer Review
    DOI:  https://doi.org/10.3346/jkms.2026.41.e318
  7. J Korean Med Sci. 2026 Jul 27. 41(29): e298
    Authorship, Disclosures, and Conflicts of Interest in Papers Related to the Use of Artificial Intelligence.
    Jaeryung Oh.
      As artificial intelligence (AI), particularly generative AI, is being actively introduced and utilized in medical research and manuscript writing, new challenges are emerging in academic publishing, specifically regarding author attribution, transparency, and conflicts of interest. This review examines the current status of AI use in medical publishing by focusing on three key areas: author attribution, disclosure methods regarding AI usage, and conflicts of interest. The prevailing view to date is that AI cannot be recognized as an author because it lacks the capacity to assume the responsibility that is a core requirement of authorship. Therefore, AI contributions are generally disclosed in the acknowledgment section. As AI becomes more deeply involved in the analysis and manuscript writing processes, it is expected that discussions regarding the attribution of intellectual contributions and the boundaries between tools and contributors will become more active in the future. The transparency of reporting AI usage depends on how the AI contributes to the research or manuscript. While AI used for purposes such as grammar or spelling correction is often exempt from disclosure requirements, if AI contributes more substantially to the content of the paper-such as text generation, data analysis, or code development-it is necessary to report this by indicating such details explicitly within the paper. However, stances on the level of disclosure vary among journals, such as whether to reveal all details like model specifications or prompts. Nevertheless, when generative AI is used in the research itself, detailed reporting is increasingly emphasized to ensure the reproducibility and scientific validity of the findings. Furthermore, AI adds a new dimension to conflicts of interest. This includes financial interests related to AI development, data ownership, and potential biases inherent in training datasets and algorithms. Since these factors can influence research results in subtle ways, more transparent and comprehensive disclosure of conflicts of interest in AI-based research is crucial.
    Keywords:  Artificial Intelligence; Authorship; Conflicts of Interest; Disclosure
    DOI:  https://doi.org/10.3346/jkms.2026.41.e298
  8. J Korean Med Sci. 2026 Jul 27. 41(29): e306
    Large Language Models and the Future of Peer-Reviewed Medical Publishing: Challenges, Editorial Responses, and a Framework for Responsible Integration.
    Hyejin Joo, Munkhzul Radnaabaatar, Jaehun Jung.
      The public release of large language models (LLMs) in late 2022 has fundamentally altered the landscape of scholarly medical publishing. LLMs now permeate every stage of the academic publishing pipeline, from manuscript drafting and peer review to editorial decision-making, with evidence suggesting that at least 13.5% of biomedical abstracts published in 2024 showed detectable LLM involvement. This rapid adoption has intersected with pre-existing structural vulnerabilities, including escalating article processing charges, publish-or-perish incentives, a chronic peer reviewer shortage, and inadequate editorial resources, creating interconnected challenges that affect all stakeholders. The response from the scholarly publishing community has been substantial but fragmented. International standards bodies, such as the International Committee of Medical Journal Editors (ICMJE), Committee on Publication Ethics (COPE), World Association of Medical Editors (WAME), have established consensus principles prohibiting AI authorship and requiring disclosure, yet individual journals range from restrictive to actively encouraging in their AI policies, and detection-based enforcement approaches have proven fundamentally unreliable, with documented biases against non-native English speakers. This narrative review characterizes the structural challenges of LLM adoption in medical publishing, provides a systematic comparison of editorial policies across major medical journals and publishers, and examines the limitations of current detection and enforcement mechanisms. Rather than focusing on prohibition and policing, the review proposes a forward-looking framework centered on three concrete proposals: 1) a three-tiered disclosure system (assistive, augmentative, substantive) integrated with the CRediT taxonomy; 2) a four-stage artificial intelligence co-editor model for deploying LLM-based tools within editorial workflows under transparent governance principles; and 3) equity-conscious policy design with international coordination through a proposed global editorial summit. The framework aims to shift the editorial paradigm from reactive enforcement to proactive governance, addressing the operational reality that editors are overburdened and underresourced for the expanding expectations placed upon them.
    Keywords:  Artificial Intelligence; Editorial Policy; Large Language Models; Medical Publishing; Peer Review; Research Integrity
    DOI:  https://doi.org/10.3346/jkms.2026.41.e306
  9. Med J Islam Repub Iran. 2026 ;40 47
    Prevalence and Content of Generative Artificial Intelligence Use Policies in Iranian Medical Science Journals.
    Maral Alipour-Tehrani, Seyede Mahsa Hojati, Homa Arshadi, Mohammadamin Erfanmanesh.
       Background: The rapid growth of generative artificial intelligence (AI) tools has created new opportunities for scientific publishing while also introducing challenges related to research integrity and editorial ethics. Although awareness of these opportunities and challenges is increasing globally, there is limited evidence regarding how Iranian journals have addressed the use of AI through formal policies in scientific publishing. This study investigates the prevalence and content of AI use policies in Iranian medical science journals.
    Methods: A cross-sectional descriptive study was conducted using medical journals listed in the Iranian Research Information System (IRIS). Of the 429 journals initially identified, inactive or inaccessible journals were excluded, resulting in a final sample of 411 journals. From August to October 2025, each journal website was manually examined using a structured checklist designed to assess 25 variables related to the existence and specific content of AI use policies. The data were analyzed using descriptive and inferential statistical methods in Microsoft Excel and SPSS.
    Results: Among 411 journals, 117 (28.5%) had a publicly accessible AI use policy. The adoption of such policies was significantly more prevalent in English-language journals, those indexed in major international databases, and journals affiliated with the Committee on Publication Ethics (COPE). Among the journals with policies, the most commonly included elements were mandatory disclosure of AI use (99.1%), clarification of permission type (98.3%), and author responsibility (88.0%). In contrast, only a small proportion of journals specified the consequences of policy violations or provided guidance on AI-assisted translation.
    Conclusion: The findings indicate that AI policy development within Iranian medical science journals remains in its nascent stages. It is essential to establish clearer and more comprehensive AI usage policies to promote transparency, accountability, and responsible AI practices in scientific publishing.
    Keywords:  Editorial Policy; Generative Artificial Intelligence; Iran; Medical Science Journals; Publication Ethics
    DOI:  https://doi.org/10.47176/mjiri.40.47
  10. J Korean Med Sci. 2026 Jul 27. 41(29): e304
    Proper Utilization of Generative AI in Scholarly Writing: The Researcher and Author Perspective.
    Dong Hoon Suh, Dong Soo Han.
      The use of generative artificial intelligence (AI) in scholarly publishing is expanding rapidly, yet clear standards for its appropriate use and disclosure remain lacking. Surveys indicate that many researchers already use AI tools for manuscript preparation, particularly for writing assistance and error detection. However, attitudes toward acceptable AI use and disclosure requirements remain inconsistent, especially regarding the use of AI in drafting manuscripts and in the peer-review process. Analyses of manuscript submissions to major journal groups suggest that the proportion of authors disclosing AI use is substantially lower than estimates from researcher surveys, indicating possible underreporting or uncertainty about reporting requirements. It is therefore essential to implement standardized frameworks that require explicit disclosure of AI use to ensure transparency and uphold trust in scholarly communication.
    Keywords:  Artificial Intelligence; Disclosure Standard; Research Integrity; Scholarly Publishing
    DOI:  https://doi.org/10.3346/jkms.2026.41.e304
  11. Occup Med (Lond). 2026 May 01. 76(4): 297-298
    Exploring the use of generative AI in the peer review process: effectiveness, verification and confidentiality.
    Saman Zaboly.
      
    DOI:  https://doi.org/10.1093/occmed/kqag045
  12. Arq Bras Cardiol. 2026 ;pii: S0066-782X2026000800201. [Epub ahead of print]123(8): e20260338
    Scientific Publishing in the era of Artificial Intelligence: Integrity and Responsibility.
    Mário Martins Oliveira, Gláucia Maria Moraes de Oliveira.
      
    DOI:  https://doi.org/10.36660/abc.20260338
  13. Nicotine Tob Res. 2026 Jul 28. pii: ntag160. [Epub ahead of print]
    What does generative AI mean for the future of nicotine and tobacco scientific publishing?
    Cailtin Notley.
      
    DOI:  https://doi.org/10.1093/ntr/ntag160
  14. Transpl Int. 2026 ;39 17015
    AI in transplantation: levelling the playing field and raising the methodological standards.
    Nina Pilat, Maria Irene Bellini, Thierry Berney, Olivier Aubert.
      
    Keywords:  artificial intelligence (AI); methodological standards; research equity; scientific publishing; transplantation medicine
    DOI:  https://doi.org/10.3389/ti.2026.17015
  15. J Am Acad Orthop Surg. 2026 Jul 31.
    Comparing Human and AI-Generated Editorials in Orthopaedic Oncology: A Blinded Evaluation of Quality and Authorship Perception.
    Jessa Fogel, Ahlam Ashkar, Philip Khoury, Thomas Schubert, Vincent Ng, Anthony Bozzo, Alexander Christ, Peter Choong, Marcos Galli Serra, Juan Pretell, Raja Bhaskara Rajasekaran, Venkatesan Kumar, Adam Levin, Kenneth Gundle, Christopher Johnson, Ricardo Becker, Alexander Lazarides, Ahmed El Ghoneimy, Panagiotis Tsagkozis, Felasfa Wodajo, Roberto Vélez, Dimosthenis Andreou, Andreas Leithner, Lisa Ercolano, Michelle Ghert.
       BACKGROUND: As the use of artificial intelligence (AI) and large language models (LLMs) is increasingly adopted into scientific writing, it is important to understand AI's ability to produce clear and accurate content that is on par with human-authored content in the field of orthopaedics, including orthopaedic oncology. The aim of this study was to compare a series of editorials written by orthopaedic oncologists with those written by a single LLM (ChatGPT 4.0) using a variety of quality metrics.
    METHODS: Volunteer orthopaedic oncologists submitted a 3- to 4-paragraph persuasive editorial on a topic of their choice in the field of musculoskeletal oncology. ChatGPT 4.0 was then prompted to write a corresponding editorial for each topic. Each editorial was evaluated by two blinded peer reviewers and graded using a 25-point scale on the following quality metrics: content, clarity, grammar, persuasiveness, and creativity. The evaluators were also asked to indicate whether they believed the editorials were written by humans or by AI.
    RESULTS: A total of 20 editorials were submitted by human authors and matched with 20 prompted AI editorials. No notable difference in average total quality score for human versus AI submissions was observed. AI-generated articles scored markedly higher in grammar, but there were no notable differences in any other quality metric. Reviewers correctly identified author type 59% of the time.
    DISCUSSION: LLMs such as ChatGPT can generate editorial content in orthopaedic oncology that matches human-written quality, suggesting a potential supportive role for AI in scientific communication, with implications for authorship standards, editorial practices, and peer review.
    DOI:  https://doi.org/10.5435/JAAOS-D-25-01565
  16. J Am Acad Dermatol. 2026 Jul 30. pii: S0190-9622(26)03205-6. [Epub ahead of print]
    Pitfalls in the Use of Artificial Intelligence in Dermatology Publishing: Important Considerations for Authors.
    Luke C Moore, Ziyang Xu, Jane M Grant-Kels, Adam I Rubin.
      
    Keywords:  AI; Artificial Intelligence; bias; disclosure; hallucinations; technology
    DOI:  https://doi.org/10.1016/j.jaad.2026.07.094
  17. Med Trop Sante Int. 2026 Jun 30. pii: mtsi.v6i2.2026.843. [Epub ahead of print]6(2):
    [The benefits and limitations of artificial intelligence in scientific writing: beware of fraud].
    Jean-Philippe Chippaux.
      
    DOI:  https://doi.org/10.48327/mtsi.v6i2.2026.843
  18. Eur Heart J Imaging Methods Pract. 2026 Aug;4(3): qyag127
    At the heart of peer review: why artificial intelligence concordance misses the human pulse.
    Zekai Yu.
      
    DOI:  https://doi.org/10.1093/ehjimp/qyag127
  19. J Korean Med Sci. 2026 Jul 27. 41(29): e281
    Comprehensive Consideration of Ethics in AI-assisted Scientific Writing and Peer Review.
    Jin-Hong Yoo.
      The emergence of large language models and generative artificial intelligence (AI) is driving fundamental transformations in the ecosystem of scholarly publishing and peer review. As manuscript production enters an era of sophisticated technological assistance, it has become imperative to transition from traditional approaches focused on misconduct prevention toward a more proactive ethical framework. We propose a new standard centered on transparency, accountability, and confidentiality, presenting a clear solution to challenges associated with integrating AI into academic discourse. Regarding authorship, AI cannot be credited as an author; the final accountability for academic integrity lies only with human authors. Transparency is maintained through a tiered disclosure framework that mandates reporting based on the extent of artificial intelligence utilization. In the context of peer review, while the potential of artificial intelligence to optimize efficiency is recognized, its application must be restricted to a closed security system to safeguard against data breaches. Furthermore, this review highlights new risk factors such as algorithmic sycophancy and prompt injection attacks, emphasizing that a final verification through human expertise is essential to ensuring the integrity of the peer review process. In conclusion, we present a comprehensive regulatory revision roadmap integrating the authors' obligations for transparent information disclosures, reviewers' commitment to confidentiality and security, and editors' ethical oversight. This framework does not regard AI as an object of absolute prohibition but rather positions it as an advanced scholarly aid rooted in human intellectual accountability. The perspectives in this Special Issue will provide a practical framework to maintain academic rigor and enhance institutional trust in the era of AI.
    Keywords:  AI; Artificial Intelligence; Large Language Model (LLM)
    DOI:  https://doi.org/10.3346/jkms.2026.41.e281
  20. Orthop Traumatol Surg Res. 2026 Jul 29. pii: S1877-0568(26)00228-8. [Epub ahead of print] 104807
    Why serve as a reviewer for an orthopaedic journal?
    Charles Pioger, Renaud Siboni, François Loisel, Philippe Clavert.
      
    Keywords:  AI-assisted peer review (LLM-generated manuscripts/plagiarism screening/editorial prescreening/human judgment); Ethical reviewing (confidentiality/conflicts of interest/COPE guidelines/reviewer bias); Peer-review quality control (manuscript selection/methodological flaws/scientific rigor); Reviewer pool imbalance (submission volume/reviewer fatigue/collective overuse); Reviewer professional development (early access to research/methodological skills/editorial board pathway/academic recognition); Reviewer training (structured manuscript evaluation/ inter-reviewer agreement)
    DOI:  https://doi.org/10.1016/j.otsr.2026.104807
  21. Phys Eng Sci Med. 2026 Aug 01.
    The declaration of Adelaide white paper: guidelines for author responsibility in peer review.
    Loredana G Marcu, Chris Clement, Anna Friedl, Richard Harbron, Werner Rühm, Magdalena Stoeva, Michael Thorne, Iuliana Toma-Dasu, Jamie Trapp, Andrzej Wojcik, Gayle Woloschak.
      
    DOI:  https://doi.org/10.1007/s13246-026-01754-y
  22. bioRxiv. 2026 Jul 20. pii: 2026.07.17.739022. [Epub ahead of print]
    Who Funds Open Data Sharing? Analysis of data availability statements in biomedical publications.
    Josh Lawrimore, Christoph Li, Dustin Moraczewski, Jean-Baptiste Poline, Adam Thomas.
      Open data sharing is increasingly mandated by research funders, journals, and institutions, yet large-scale compliance measurement remains challenging. We analyzed 951,949 open access biomedical research articles published between January 2024 and June 2025 using a dual-source pipeline: PDF-based text extraction (MinerU) where PDFs were available and PMC XML otherwise, followed by algorithmic detection of data sharing statements (oddpub v7.2.3), enriched with funder, journal, and institutional metadata from OpenAlex. The corpus included 294,172 PDF-covered articles (30.9%) and 657,777 XML-only articles (69.1%). We found an over-all open data rate of 8.7%, rising to 11.7% among funder-linked articles (those with at least one funder identified in the metadata). Rates varied more than tenfold across the research ecosystem: leading major funders reached observed open data rates of 20-24%, while top journals reached observed rates of 70-86%, with corrected estimates as high as 92.9% (Nature Genetics) after adjusting for XML-only coverage limitations. PDF-based detection identified approximately 52% more data sharing statements than XML-based methods on the same articles. These observed rates differ markedly across funders and journals, and current overall sharing remains far below universal compliance. These patterns provide an empirical baseline against which future policy changes can be measured. An interactive dashboard at https://www.opensciencemetrics.org enables stakeholders to explore and benchmark these results.
    DOI:  https://doi.org/10.64898/2026.07.17.739022
  23. J Mil Veteran Fam Health. 2025 Sep;11(4): 1-2
    From data sharing to knowledge co-creation and evidence-based practice: Advancing military and Veteran health research.
    Stephanie A H Belanger, Nicholas Held, Ronald Shore.
      
    DOI:  https://doi.org/10.3138/jmvfh-2025-0523
  24. Toxicol Sci. 2026 Aug 01. pii: kfag084. [Epub ahead of print]209(8):
    How to be seen: optimizing your manuscript for AI search.
    Alison Harrill.
      
    DOI:  https://doi.org/10.1093/toxsci/kfag084
  25. Int J Obstet Anesth. 2026 Jul 13. pii: S0959-289X(26)00407-3. [Epub ahead of print]68 105249
    Large language models in academic writing: when Letters to the Editor stop serving scientific discourse.
    Carolyn F Weiniger, Ruth Landau.
      
    DOI:  https://doi.org/10.1016/j.ijoa.2026.105249
  26. Eur J Cancer. 2026 Jul 24. pii: S0959-8049(26)00744-6. [Epub ahead of print]245 116963
    Temporal linguistic shifts in oncology randomized controlled trials following large language model availability: A corpus analysis of 21,392 publications.
    Amina Silva, Vanessa Silva E Silva.
       BACKGROUND: Public availability of large language models (LLMs) from late 2022 has raised concerns about AI-assisted writing in scientific publishing. Oncology randomized controlled trials (RCTs) underpin cancer treatment guidelines and regulatory decisions worldwide, yet whether linguistic changes have followed the emergence of LLMs has not been examined at scale.
    METHODS: We conducted a retrospective corpus linguistics analysis of 21,392 oncology RCTs indexed in PubMed (2019-2026), stratified into pre-LLM (2019-2022; n = 11,308) and post-LLM (2023-2026; n = 10,084) eras. Full text was retrieved for 10,483 papers (49.0%) via PubMed Central; abstracts were used otherwise. The primary outcome was the frequency of 34 formulaic phrases documented as disproportionately prevalent in post-LLM biomedical text. A secondary outcome applied the GRIM test. Mann-Whitney U tests, chi-squared analyses, and multivariable linear regression.
    RESULTS: Post-LLM papers contained significantly more formulaic phrases than pre-LLM papers (mean 0.96 [SD 1.50] vs. 0.65 [SD 1.14]; U=51,334,465, Z = -14.56, p < 0.001; r = 0.10). The increase was consistent across full-text and abstract-only subgroups and largest in discussion sections (1.23 vs. 0.88; +40%). Year-by-year analysis showed a progressive rise from 2019 (0.45) through 2025 (1.13). GRIM results were limited by data availability (48 papers; null result, p = 0.085).
    CONCLUSION: This study identified a statistically significant increase in formulaic linguistic patterns in oncology RCTs following the public release of LLMs. Although these population-level findings cannot confirm AI-assisted writing in individual papers or authors, they are consistent with increasing LLM use and highlight the need for transparent AI disclosure, ongoing surveillance of scientific writing, and evidence-informed editorial policies.
    Keywords:  Artificial intelligence; Corpus linguistics; Large language models; Oncology; Randomized controlled trials; Research integrity; Scientific writing
    DOI:  https://doi.org/10.1016/j.ejca.2026.116963
  27. Evid Based Nurs. 2026 Jul 31. pii: ebnurs-2026-104744. [Epub ahead of print]
    Writing for publication helps reduce the theory-practice gap and all nurses have a role to play.
    Michael J Tatterton, Ben Parkinson.
      
    Keywords:  Evidence-Based Nursing; Nursing; Nursing Research
    DOI:  https://doi.org/10.1136/ebnurs-2026-104744
  28. Pan Afr Med J. 2026 ;53 133
    Journal response time: an experience at Pan African Medical Journal.
    Gina Joubert.
      This comment outlines the author´s experience with a submission to the Pan African Medical Journal. Twenty-two months after submission, the manuscript remains in review, and no first decision from the journal has been received. The author urges the editorial office to put measures in place to ensure that other authors do not have a similar experience.
    Keywords:  Journal response time; Pan African Medical Journal; comment; communication; review
    DOI:  https://doi.org/10.11604/pamj.2026.53.133.49254
  29. Swiss Med Wkly. 2026 Aug 01. 156 5646
    The Diamond road to Open Science.
    Patrick Furrer, Carlos Quinto.
      Diamond Open Access is the fairest and most sustainable model for scientific communication. The Diamond model promotes editorial independence, supports researchers by waiving article processing charges, and serves as an indispensable public research infrastructure. "Read & Publish" agreements increase dependence on large commercial publishers, and some of the funds currently spent on such agreements could instead be used to provide long-term support for Diamond journals. Promising models for sustainable funding and collaboration are emerging through national and international initiatives. However, Switzerland is already providing concrete evidence that institutions do not have to wait until national mechanisms are fully developed. In 2025, the University of Lausanne launched the "Diamond Open Access Fund", a pioneering initiative of its kind in Switzerland. The future development of Diamond Open Access should focus not only on sustainable funding but also on enhancing the quality, transparency, and culture of scholarly publishing. As Switzerland's only fully peer-reviewed English-language Diamond medical journal, the Swiss Medical Weekly (SMW) provides an essential platform for clinically relevant Swiss research, offering international visibility and immediate open access without author fees or embargoes. SMW will continue to work towards modernising its quality assurance processes and contributing to international developments in the field of Open Science, with a view to strengthening transparency, reproducibility, and relevance in medical research.
    DOI:  https://doi.org/10.57187/5646
  30. Asia Pac J Clin Nutr. 2026 Aug;35(4): 573-576
    Guidelines on the use of Artificial Intelligence for authors, reviewers, and editors of APJCN.
    Editorial Committee of Asia Pacific Journal of Clinical Nutrition
      Generative artificial intelligence (AI) is an emerging technology with substantial potential to support scientific research. Scientific communities across various disciplines are actively exploring its capabilities, including its application in scientific writing, peer review, and editorial processes. However, the inappropriate use of AI may lead to academic misconduct and compromise research integrity. To promote the responsible and ethical use of AI by authors, reviewers, and editors, APJCN has developed these guidelines. The guidelines are based on widely accepted ethical principles within the scientific publishing community. Given the rapid evolution of generative AI technologies, these guidelines may be updated and revised as necessary.
    DOI:  https://doi.org/10.6133/apjcn.202608_35(4).0002
  31. Med J Islam Repub Iran. 2025 ;39 169
    The 39th Anniversary of the Medical Journal of the I.R of Iran.
    Hamid R Baradaran, Marzieh Khadem Sharif.
      
    DOI:  https://doi.org/10.47176/mjiri.39.169
  32. Ann Surg Oncol. 2026 Jul 30.
    ASO Author Reflections: Artificial Intelligence in the Scientific Merit Review Process.
    Lauren N Cohen, Drake Seibert, Anai N Kothari, Chandler S Cortina.
      
    DOI:  https://doi.org/10.1245/s10434-026-20290-3
  33. J Stomatol Oral Maxillofac Surg. 2026 Jul 27. pii: S2468-7855(26)00220-X. [Epub ahead of print] 102924
    Publication Fragmentation in Oral and Maxillofacial Surgery: Efficient Knowledge Dissemination or Ethical Redundancy?
    Jignesh Rajguru, Ramakrishna Shenoi.
      
    DOI:  https://doi.org/10.1016/j.jormas.2026.102924
  34. Acad Med. 2026 Jul 29. pii: wvag231. [Epub ahead of print]
    Auld Lang Syne: reflections and opportunities for engagement and growth on the 100th anniversary of Academic Medicine.
    Mary Ellen J Goldhamer.
      
    Keywords:  medical education; peer review
    DOI:  https://doi.org/10.1093/acamed/wvag231
  35. J Am Coll Radiol. 2026 Jul 27. pii: S1546-1440(26)00372-8. [Epub ahead of print]
    Implementing a Department-wide Peer Learning Program Across a Large Academic Radiology Practice.
    Firouzeh K Arjmandi, Lakshmi Ananthakrishnan, Alireza Eajazi, Kiran Batra, Travis Browning, Oganes Ashikyan, Katherine Hyde, Jessica H Porembka.
      
    Keywords:  OPPE; Peer learning; peer review; quality improvement
    DOI:  https://doi.org/10.1016/j.jacr.2026.07.013
  36. Rev Esp Cir Ortop Traumatol. 2026 Jul 29. pii: S1888-4415(26)00157-8. [Epub ahead of print]
    RECOT looks to the future: internationalisation, excellence and scientific impact.
    Mario Herrera-Pérez.
      
    DOI:  https://doi.org/10.1016/j.recot.2026.07.012
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