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This paper is in the following e-collection/theme issue:

Artificial Intelligence (2469) Clinical Informatics (1503) Clinical Information and Decision Making (2157) Generative Language Models Including ChatGPT (894) Applications of AI (389) Foundation Models and Their Applications in AI (68)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76048, first published .
Three doctors in white coats smiling and looking at a tablet.

Fine-Tuning Methods for Large Language Models in Clinical Medicine by Supervised Fine-Tuning and Direct Preference Optimization: Comparative Evaluation

Fine-Tuning Methods for Large Language Models in Clinical Medicine by Supervised Fine-Tuning and Direct Preference Optimization: Comparative Evaluation

Authors of this article:

Thomas Savage1 Author Orcid Image ;   Stephen P Ma2 Author Orcid Image ;   Abdessalem Boukil3 ;   Ekanath Rangan4 Author Orcid Image ;   Vishwesh Patel5 Author Orcid Image ;   Ivan Lopez4, 6 Author Orcid Image ;   Jonathan Chen2, 6, 7, 8 Author Orcid Image
Article Authors Cited by (3) Tweetations (3) Metrics

Journals

  1. Naderi B, Liu L, Ghandehari A, Khoshons D, Andrew Taylor R, Bhavsar N, Balasubramanian S, Tanouye R, Creech N, Davidson C, Norden J, Sharma R, Fortenko A. The role of large language models in emergency care: a comprehensive benchmarking study. npj Artificial Intelligence 2026;2(1) View
  2. Xiao H, Hui N, Xu Y, Li Z, Peng J. Medical multimodal large language models: A survey. Information Fusion 2026;134:104386 View
  3. Feng H, Wang X. Comparative performance of four large language models in generating evidence-based exercise prescriptions using FITT-VP framework. Frontiers in Physiology 2026;17 View