Advanced analysis of leading large language models for diagnostic accuracy in retinal imaging

Abstract

Current LLMs show promising but limited capabilities in ophthalmological image interpretation. While performance on common conditions like retinal detachments and age-related macular degeneration is moderately good, significant challenges remain with rare conditions, myopic pathologies and complex vascular disorders. The competitive performance between GPT-4.5 and Gemini 2.0 Pro, with each excelling in different pathology categories, suggests that leveraging their complementary strengths might offer improved diagnostic support.

Ähnliche Arbeiten