Testing and evaluation of generative large language models in electronic health record applications: a systematic review
Our findings highlight the need to evaluate generative LLMs on EHR data across a broader range of clinical specialties and tasks, as well as the urgent need for standardized, scalable, and clinically meaningful evaluation frameworks.
Authors
- Yifei Wang (ORCID: https://orcid.org/0009-0009-1890-9232)
- D. W. Bates
- Xinsong Du (ORCID: https://orcid.org/0000-0003-3713-3264)
- Wenyu Zhang (ORCID: https://orcid.org/0000-0002-2507-3033)
- Xinyu Chen (ORCID: https://orcid.org/0000-0001-5816-8621)
- Ting Wang (ORCID: https://orcid.org/0000-0001-7414-5390)
- Hao Guan (ORCID: https://orcid.org/0000-0002-8117-4584)
- Richard Yang (ORCID: https://orcid.org/0009-0008-8283-4952)
- Yiming Li (ORCID: https://orcid.org/0009-0009-8784-1745)
- Pengyu Hong
- John Lian
- Li Zhou
- Ya-Wen Chuang
- Zhengyang Zhou
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- National Chung Hsing University (TW)
- China Medical University (TW)
- Taichung Veterans General Hospital (TW)
- Brandeis University (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-12-23
- DOI
- https://doi.org/10.1093/jamia/ocaf233
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 9.92