Evaluation of large language models in patient education and clinical decision support for rotator cuff injury: a two-phase benchmarking study
The study found that LLMs, particularly ChatGPT-4o and Gemini, excelled in understanding rotator cuff injury-related knowledge and responding to patients, showing strong potential for further development.
Authors
- Dao-Fang Ding (ORCID: https://orcid.org/0000-0003-4056-142X)
- Zan Nie (ORCID: https://orcid.org/0000-0002-9222-1462)
- Jiechao Zhang
- Xinfeng Tang (ORCID: https://orcid.org/0000-0002-0715-5529)
- Wei Wei (ORCID: https://orcid.org/0000-0002-4386-8577)
- Qian Zhang (ORCID: https://orcid.org/0000-0003-4907-7423)
- Yilin Wang (ORCID: https://orcid.org/0000-0001-5320-0274)
- Jian Meng (ORCID: https://orcid.org/0000-0002-4161-5680)
- Yong‐Ming He (ORCID: https://orcid.org/0000-0003-3154-2892)
- Li-Chao Tian
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-08-04
- DOI
- https://doi.org/10.1186/s12911-025-03105-5
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65
Funders
- Science and Technology Commission of Shanghai Municipality