Journal article : Review
The TRIPOD-LLM reporting guideline for studies using large language models
- Abstract:
- Large language models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present transparent reporting of a multivariable model for individual prognosis or diagnosis (TRIPOD)-LLM, an extension of the TRIPOD + artificial intelligence statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion. The guidelines introduce a modular format accommodating various LLM research designs and tasks, with 14 main items and 32 subitems applicable across all categories. Developed through an expedited Delphi process and expert consensus, TRIPOD-LLM emphasizes transparency, human oversight and task-specific performance reporting. We also introduce an interactive website ( https://tripod-llm.vercel.app/ ) facilitating easy guideline completion and PDF generation for submission. As a living document, TRIPOD-LLM will evolve with the field, aiming to enhance the quality, reproducibility and clinical applicability of LLM research in healthcare through comprehensive reporting.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
Actions
Access Document
- Files:
-
-
(Preview, Accepted manuscript, pdf, 360.0KB, Terms of use)
-
- Publisher copy:
- 10.1038/s41591-024-03425-5
Authors
+ Cancer Research UK
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- Funder identifier:
- https://ror.org/054225q67
- Grant:
- 27294
+ U.S. National Science Foundation
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- Funder identifier:
- https://ror.org/021nxhr62
- Grant:
- 1928614
- 2129076
+ Engineering and Physical Sciences Research Council
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- Funder identifier:
- https://ror.org/0439y7842
- Grant:
- EP/Y018516/1
- Publisher:
- Springer Nature
- Journal:
- Nature Medicine More from this journal
- Volume:
- 31
- Issue:
- 1
- Pages:
- 60-69
- Place of publication:
- United States
- Publication date:
- 2025-01-08
- Acceptance date:
- 2024-11-21
- DOI:
- EISSN:
-
1546-170X
- ISSN:
-
1078-8956
- Pmid:
-
39779929
- Language:
-
English
- Subtype:
-
Review
- Pubs id:
-
2077445
- Local pid:
-
pubs:2077445
- Deposit date:
-
2025-03-17
Terms of use
- Copyright holder:
- Gallifant et al
- Copyright date:
- 2025
- Rights statement:
- © 2025, The Author(s), under exclusive licence to Springer Nature America, Inc.
- Notes:
-
This is the accepted manuscript version of the article. The final version is available online from Springer Nature at https://dx.doi.org/10.1038/s41591-024-03425-5
The author accepted manuscript (AAM) of this paper has been made available under the University of Oxford's Open Access Publications Policy, and a CC BY public copyright licence has been applied.
- Licence:
- CC Attribution (CC BY)
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