Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/70481, first published .
How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective

How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective

How to Design, Create, and Evaluate an Instruction-Tuning Dataset for Large Language Model Training in Health Care: Tutorial From a Clinical Perspective

Journals

  1. Pierri M, Galeazzi M, D’Alessio S, Dottori M, Capodaglio I, Corinaldesi C, Marini M, Di Eusanio M. Evaluating Large Language Models in Cardiology: A Comparative Study of ChatGPT, Claude, and Gemini. Hearts 2025;6(3):19 View
  2. Alter I, Chan K, Andreadis K, Rameau A. Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review. The Laryngoscope 2025 View
  3. Chang Q, Chen F, Chen Y, Cheng L, Dong D, Dong J, Feng X, Ge J, He J, He Y, He Z, Ji H, Jiang X, Jiang Z, Li N, Li P, Li Y, Liu B, Liu J, Lyu H, Min D, Qi W, Shen X, Sheng B, Sun J, Sun Y, Tian B, Wang K, Wang L, Wang L, Wang W, Wang Y, Wang Y, Wang Z, Weng J, Wei J, Wu G, Wu X, Xiao Y, Xu Y, Yan P, Ye Z, Yin W, Zhang C, Zhang D, Zhang P, Zhang W, Zhang X, Zhao S, Zhao Y, Zhou S, Zhou X, Zhu B, Zhu L, Zhu Z. 2025 Expert consensus on retrospective evaluation of large language model applications in clinical scenarios. Intelligent Medicine 2025 View