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Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/76048, first published .
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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

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
  4. Ong W, Tan G, Ting Y, Ge S, Tan Y, Low X, Tan W, Makmur A, Leow N, Din Abdul Jabbar M, Yap Q, Ong S, Tan J, Kumar N, Hallinan J. Automating the Management of Extraspinal Findings in MRI Spine Studies Using a Privacy-Preserving Large Language Model: A Single-Institution Feasibility Study (Preprint). Journal of Medical Internet Research 2025 View
  5. Ahmed S, Yousuf Sadeque F. Clinical Note Generation From Doctor-Patient Conversations Using Parameter-Efficient Fine-Tuning Large Language Models: Comparative Study. JMIR Medical Informatics 2026;14:e82545 View