Generative Artificial Intelligence Tools in Medical Research (GAMER): Protocol for a Scoping Review and Development of Reporting Guidelines
Bibliographic record
Abstract
BACKGROUND: The integration of artificial intelligence (AI) has revolutionized medical research, offering innovative solutions for data collection, patient engagement, and information dissemination. Powerful generative AI (GenAI) tools and other similar chatbots have emerged, facilitating user interactions with virtual conversational agents. However, the increasing use of GenAI tools in medical research presents challenges, including ethical concerns, data privacy issues, and the potential for generating false content. These issues necessitate standardization of reporting to ensure transparency and scientific rigor. OBJECTIVE: The development of the Generative Artificial Intelligence Tools in Medical Research (GAMER) reporting guidelines aims to establish comprehensive, standardized guidelines for reporting the use of GenAI tools in medical research. METHODS: The GAMER guidelines are being developed following the methodology recommended by the Enhancing the Quality and Transparency of Health Research (EQUATOR) Network, involving a scoping review and expert Delphi consensus. The scoping review searched PubMed, Web of Science, Embase, CINAHL, PsycINFO, and Google Scholar (for the first 200 results) using keywords like "generative AI" and "medical research" to identify reporting elements in GenAI-related studies. The Delphi process involves 30-50 experts with ≥3 years of experience in AI applications or medical research, selected based on publication records and expertise across disciplines (eg, clinicians and data scientists) and regions (eg, Asia and Europe). A 7-point-scale survey will establish consensus on checklist items. The testing phase invites authors to apply the GAMER checklist to GenAI-related manuscripts and provide feedback via a questionnaire, while experts assess reliability (κ statistic) and usability (time taken, 7-point Likert scale). The study has been approved by the Ethics Committee of the Institute of Health Data Science at Lanzhou University (HDS-202406-01). RESULTS: The GAMER project was launched in July 2023 by the Evidence-Based Medicine Center of Lanzhou University and the WHO Collaborating Centre for Guideline Implementation and Knowledge Translation, and it concluded in July 2024. The scoping review was completed in November 2023. The Delphi process was conducted from October 2023 to April 2024. The testing phase began in March 2025 and is ongoing. The expected outcome of the GAMER project is a reporting checklist accompanied by relevant terminology, examples, and explanations to guide stakeholders in better reporting the use of GenAI tools. CONCLUSIONS: GAMER aims to guide researchers, reviewers, and editors in the transparent and scientific application of GenAI tools in medical research. By providing a standardized reporting checklist, GAMER seeks to enhance the clarity, completeness, and integrity of research involving GenAI tools, thereby promoting collaboration, comparability, and cumulative knowledge generation in AI-driven health care technologies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/64640.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.197 | 0.309 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".