Large language model based simplification of ophthalmological clinical and ancillary results for patient readability.

The original reports were highly complex, with a median aRGL of 13.8 overall, 11.6 for fundus reports, and 15.4 for ocular ultrasound reports.

Prompt engineering was a major determinant of performance, and the targeted 7th-grade prompt produced the best results across models.

Copilot, Gemini, and GPT-4.0 achieved median aRGL values closest to the recommended patient-facing reading level, while GPT-3.5 showed weaker performance in some comparisons.

Clinical validation showed substantial agreement between raters (kappa range, 0.70-0.97).