LEME: Open Large Language Models for Ophthalmology with Advanced Reasoning and Clinical Validation

Understand

The rising prevalence of eye diseases poses a growing public health burden.

• Large language models (LLMs) offer a promising path to reduce documentation workload and support clinical decision-making.
• However, few have been tailored for ophthalmology, and most evaluations focus mainly on knowledge-based QA without clinically relevant benchmarks or real-world validation.
• Here, we present LEME, a suite of open-weight LLMs developed through a two-stage process: (1) instruction tuning on 200,000 samples from clinical guidelines, textbooks, and case reports to enhance reasoning and task-following, and (2) reinforcement learning with ~30,000 preference labels to enhance accuracy and informativeness.