Comment on: Evaluating large language models vs residents in cataract and refractive surgery: comparative analysis using the American Academy of Ophthalmology Self-Assessment Program
M Vijayasimha, M Srikanth, Logesh BabuPurpose:
To critically evaluate new evidence regarding big language models (LLMs) in cataract and refractive surgery and suggest a clinically responsible validation system.
Methods:
Conceptual synthesis of the recent literature of high impact and comment on a recent JCRS study on the performance of LLM compared to ophthalmology residents.
Findings:
Despite the high benchmark performance of LLMs, there are still severe deficits in clinical reliability, reproducibility, citation traceability, and safety in anterior-segment procedures. It proposes a three-layer validation pipeline, which includes domain-specific benchmarking, multi-dimensional performance reporting, and use-case-based deployment thresholds.
Conclusions:
It is necessary to shift the accuracy of examination to responsible clinical integration. An equity-sensitive validation framework applicable globally can make the application of AI in cataract and refractive surgery safe, transparent, and effective.