Xufei Luo, Yih Chung Tham, Mauro Giuffrè, Robert Ranisch, Mohammad Daher, Kyle Lam, Alexander Viktor Eriksen, Che-Wei Hsu, Akihiko Ozaki, Fabio Ynoe de Moraes, Sahil Khanna, Kuan-Pin Su, Emir Begagić, Zhaoxiang Bian, Yaolong Chen, Janne Estill
{"title":"在医学研究中使用生成式人工智能工具的报告指南：GAMER声明。","authors":"Xufei Luo, Yih Chung Tham, Mauro Giuffrè, Robert Ranisch, Mohammad Daher, Kyle Lam, Alexander Viktor Eriksen, Che-Wei Hsu, Akihiko Ozaki, Fabio Ynoe de Moraes, Sahil Khanna, Kuan-Pin Su, Emir Begagić, Zhaoxiang Bian, Yaolong Chen, Janne Estill","doi":"10.1136/bmjebm-2025-113825","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles.</p><p><strong>Design and setting: </strong>International online Delphi study.</p><p><strong>Participants: </strong>International experts in medicine and artificial intelligence.</p><p><strong>Main outcome measures: </strong>The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research).</p><p><strong>Results: </strong>The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool's role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions.</p><p><strong>Conclusion: </strong>GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.</p>","PeriodicalId":9059,"journal":{"name":"BMJ Evidence-Based Medicine","volume":" ","pages":""},"PeriodicalIF":9.0000,"publicationDate":"2025-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.\",\"authors\":\"Xufei Luo, Yih Chung Tham, Mauro Giuffrè, Robert Ranisch, Mohammad Daher, Kyle Lam, Alexander Viktor Eriksen, Che-Wei Hsu, Akihiko Ozaki, Fabio Ynoe de Moraes, Sahil Khanna, Kuan-Pin Su, Emir Begagić, Zhaoxiang Bian, Yaolong Chen, Janne Estill\",\"doi\":\"10.1136/bmjebm-2025-113825\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles.</p><p><strong>Design and setting: </strong>International online Delphi study.</p><p><strong>Participants: </strong>International experts in medicine and artificial intelligence.</p><p><strong>Main outcome measures: </strong>The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research).</p><p><strong>Results: </strong>The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool's role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions.</p><p><strong>Conclusion: </strong>GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.</p>\",\"PeriodicalId\":9059,\"journal\":{\"name\":\"BMJ Evidence-Based Medicine\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":9.0000,\"publicationDate\":\"2025-05-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"BMJ Evidence-Based Medicine\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1136/bmjebm-2025-113825\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MEDICINE, GENERAL & INTERNAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"BMJ Evidence-Based Medicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1136/bmjebm-2025-113825","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MEDICINE, GENERAL & INTERNAL","Score":null,"Total":0}
Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.
Objectives: Generative artificial intelligence (GAI) tools can enhance the quality and efficiency of medical research, but their improper use may result in plagiarism, academic fraud and unreliable findings. Transparent reporting of GAI use is essential, yet existing guidelines from journals and institutions are inconsistent, with no standardised principles.
Design and setting: International online Delphi study.
Participants: International experts in medicine and artificial intelligence.
Main outcome measures: The primary outcome measure is the consensus level of the Delphi expert panel on the items of inclusion criteria for GAMER (Rreporting guideline for the use of Generative Artificial intelligence tools in MEdical Research).
Results: The development process included a scoping review, two Delphi rounds and virtual meetings. 51 experts from 26 countries participated in the process (44 in the Delphi survey). The final checklist comprises nine reporting items: general declaration, GAI tool specifications, prompting techniques, tool's role in the study, declaration of new GAI model(s) developed, artificial intelligence-assisted sections in the manuscript, content verification, data privacy and impact on conclusions.
Conclusion: GAMER provides universal and standardised guideline for GAI use in medical research, ensuring transparency, integrity and quality.
期刊介绍：
BMJ Evidence-Based Medicine (BMJ EBM) publishes original evidence-based research, insights and opinions on what matters for health care. We focus on the tools, methods, and concepts that are basic and central to practising evidence-based medicine and deliver relevant, trustworthy and impactful evidence.
BMJ EBM is a Plan S compliant Transformative Journal and adheres to the highest possible industry standards for editorial policies and publication ethics.
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