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<p>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 (<a href="https://tripod-llm.vercel.app/">https://tripod-llm.vercel.app/</a>) 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.</p>

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The TRIPOD-LLM reporting guideline for studies using large language models

  • 期刊论文

  • 发表日期：

    2025年1月8日

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  • Jack Gallifant,
  • Majid Afshar,
  • Saleem Ameen,
  • Yindalon Aphinyanaphongs,
  • Shan Chen,
  • Giovanni Cacciamani,
  • Dina Demner-Fushman,
  • Dmitriy Dligach,
  • Roxana Daneshjou,
  • Chrystinne Fernandes,
  • Lasse Hyldig Hansen,
  • Adam Landman,
  • Lisa Lehmann,
  • Liam G. McCoy,
  • Timothy Miller,
  • Amy Moreno,
  • Nikolaj Munch,
  • David Restrepo,
  • Guergana Savova,
  • Renato Umeton,
  • Judy Wawira Gichoya,
  • Gary S. Collins,
  • Karel G. M. Moons,
  • Leo A. Celi,
  • Danielle S. Bitterman

摘要

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.