Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement

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

Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement

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^*

^*Corresponding author for this work

Research output: Contribution to journal › Journal article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Article number	bmjebm-2025-113825
Number of pages	11
Journal	BMJ Evidence-Based Medicine
DOIs	https://doi.org/10.1136/bmjebm-2025-113825
Publication status	E-pub ahead of print - 13 May 2025

User-Defined Keywords

Epidemiology
Quality of Health Care

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1136/bmjebm-2025-113825

Cite this

Luo, X., Tham, Y. C., Giuffrè, M., Ranisch, R., Daher, M., Lam, K., Eriksen, A. V., Hsu, C.-W., Ozaki, A., de Moraes, F. Y., Khanna, S., Su, K.-P., Begagić, E., Bian, Z., Chen, Y., & Estill, J. (2025). Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement. BMJ Evidence-Based Medicine, Article bmjebm-2025-113825. Advance online publication. https://doi.org/10.1136/bmjebm-2025-113825

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title = "Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement",

abstract = "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.",

keywords = "Epidemiology, Quality of Health Care",

author = "Xufei Luo and Tham, {Yih Chung} and Mauro Giuffr{\`e} and Robert Ranisch and Mohammad Daher and Kyle Lam and Eriksen, {Alexander Viktor} and Che-Wei Hsu and Akihiko Ozaki and {de Moraes}, {Fabio Ynoe} and Sahil Khanna and Kuan-Pin Su and Emir Begagi{\'c} and Zhaoxiang Bian and Yaolong Chen and Janne Estill",

note = "This study was funded by the Research Unit of Evidence-Based Evaluation and Guidelines (2021RU017), Chinese Academy of Medical Sciences, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China (2021RU017) .",

year = "2025",

month = may,

day = "13",

doi = "10.1136/bmjebm-2025-113825",

language = "English",

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T1 - Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement

AU - Luo, Xufei

AU - Tham, Yih Chung

AU - Giuffrè, Mauro

AU - Ranisch, Robert

AU - Daher, Mohammad

AU - Lam, Kyle

AU - Eriksen, Alexander Viktor

AU - Hsu, Che-Wei

AU - Ozaki, Akihiko

AU - de Moraes, Fabio Ynoe

AU - Khanna, Sahil

AU - Su, Kuan-Pin

AU - Begagić, Emir

AU - Bian, Zhaoxiang

AU - Chen, Yaolong

AU - Estill, Janne

N1 - This study was funded by the Research Unit of Evidence-Based Evaluation and Guidelines (2021RU017), Chinese Academy of Medical Sciences, School of Basic Medical Sciences, Lanzhou University, Lanzhou, Gansu, China (2021RU017) .

PY - 2025/5/13

Y1 - 2025/5/13

N2 - 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.

AB - 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.

KW - Epidemiology

KW - Quality of Health Care

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U2 - 10.1136/bmjebm-2025-113825

DO - 10.1136/bmjebm-2025-113825

M3 - Journal article

SN - 2515-446X

JO - BMJ Evidence-Based Medicine

JF - BMJ Evidence-Based Medicine

M1 - bmjebm-2025-113825

ER -

Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement

Abstract

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UN SDGs

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