Evaluating the effectiveness of large language models in patient education for conjunctivitis
Our study showed LLMs effectively improve patient education in conjunctivitis. These models showed considerable promise in real-world patient interactions. Despite encouraging results, further refinement, particularly in personalisation and handling complex inquiries, is essential prior to the clinical integration of these LLMs.
Authors
- K. Y. Shan (ORCID: https://orcid.org/0009-0001-2651-4742)
- Qihua Le (ORCID: https://orcid.org/0000-0001-7655-3101)
- Jiaxu Hong
- Xujiao Zhou (ORCID: https://orcid.org/0000-0002-9719-4889)
- Runhan Shi (ORCID: https://orcid.org/0000-0001-7352-2967)
- Zhi Chen (ORCID: https://orcid.org/0000-0002-9038-4831)
- Yao He
- Jingyuan Wang (ORCID: https://orcid.org/0009-0003-6412-9858)
Institutions
- Macau University of Science and Technology (MO)
- Fudan University (CN)
- Shanghai Eye Disease Prevention & Treatment Center (CN)
- State Key Laboratory of Synthetic Chemistry (CN)
- Children's Hospital of Fudan University (CN)
Publication Details
- Journal
- British Journal of Ophthalmology
- Published
- 2024-08-30
- DOI
- https://doi.org/10.1136/bjo-2024-325599
- Citations
- 14
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.68
Funders
- National Natural Science Foundation of China