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Danielle Bitterman: TRIPOD-LLM consensus guidelines for reporting LLM research in biomedicine
Jan 12, 2025, 15:38

Danielle Bitterman: TRIPOD-LLM consensus guidelines for reporting LLM research in biomedicine

Danielle Bitterman, Assistant Professor at Harvard Medical School, posted on LinkedIn:

TRIPOD-LLM is out in Nature Medicine!

Check out our consensus guidelines for reporting LLM research in biomedicine.

TRIPOD-LLM is intended to be a living guideline to keep up with the rapid advances in LLMs.

Kudos to lead author Jack Gallifant and many thanks to the whole team and all who provided input on the checklist.

Noteworthy points:

– Living guidelines: The checklist is designed as a living document, which will be updated on a regular basis based on a review of the literature and input from the community. This approach was taken due to the rapid developments in the field, enabling agile versioning, refinement from user testing and timely updates as the field advances.

– Task-specific guidances: The checklist includes a new section that provides task-specific guidance designed to address the particular challenges and needs associated with different LLM applications in healthcare. This addition ensures that reporting is tailored and relevant to the specific functions and objectives of the LLM under study.

– Emphasis on transparency and fairness: The new guidelines highlight the importance of recognizing and addressing societal biases that may be encoded in clinical models. The checklist integrates these concepts throughout, ensuring that bias and fairness are considered at every stage of the model’s life cycle.

– Modular framework: The new guidelines are modular, with different requirements based on the research design(s) and LLM task(s) that are reported in a given study. This change was motivated by the wide variety of applications and approaches in biomedical LLM research, from model development through evaluation, necessitating more specialized reporting items.”

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

Authors: 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 and Danielle S. Bitterman.

Danielle Bitterman

Dr. Danielle Bitterman is an Assistant Professor at Harvard Medical School and an Attending Physician at Dana-Farber Cancer Institute. She is also an Associate Editor for Radiation Oncology at HemOnc.org. As a physician-scientist, her research focuses on leveraging AI advancements to improve cancer care.