Ophtimus-V2-Tx: A Compact Domain-Specific LLM for Ophthalmic Diagnosis and Treatment Planning

Abstract

<title>Abstract</title> Recent advances in Large language models (LLMs) have shown promising results in clinical decision support, but they often fall short when applied to case-specific reasoning required in real-world medical practice. We present Ophtimus-V2-Tx, a lightweight, domain-specific Small LM (SLM) fine-tuned on over 10,000 ophthalmology case reports, built upon an 8B-parameter base model. The model is optimized to generate diagnosis and treatment suggestions aligned with standardized coding systems such as ICD-10-CM, ATC, and ICD-10-PCS. Using the CliBench framework, we evaluate its performance across four clinical tasks: primary diagnosis, secondary diagnosis, medication recommendation, and surgical procedure prediction. Ophtimus-V2- Tx achieved the highest full-code F1 score in medication (0.32) and surgical tasks (0.16), and outperformed GPT-4o in primary diagnosis (F1: 0.28 vs. 0.25). It also showed strong topic-specific performance, attaining 0.80 accuracy on multiple-choice questions related to Uveitis, a clinically complex condition. Our contributions include: (1) empirical validation of case-based fine-tuning for clinical task adaptation, (2) application of a hierarchical benchmarking framework for multi-dimensional evaluation, and (3) a reproducible pipeline for building efficient, deployable medical LLMs. These findings demonstrate the feasibility of compact, domain-adapted models in delivering competitive clinical performance.

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