Comparison of Large Language Models in Diagnosis and Management of Challenging Clinical Cases
Subscription LLMs were more prevalent among highly ranked LLMs suggesting that these perform better as ophthalmic assistants. While readability was poor for the average person, the content was understood by a board-certified ophthalmologist. The accuracy of LLMs is not high enough to recommend patient care in standalone mode, but aiding clinicians in patient care and prevent oversights is promising.
Authors
- David J. Browning (ORCID: https://orcid.org/0000-0002-0442-8122)
- S Shanmugam
Institutions
- Wake Forest University (US)
Publication Details
- Journal
- Clinical ophthalmology
- Published
- 2024-11-01
- DOI
- https://doi.org/10.2147/opth.s488232
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.60