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

Abstract 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.