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The efficacy of large language models (LLMs) in domain-specific medicine,particularly for managing complex diseases such as osteoarthritis (OA), remainslargely unexplored. This study focused on evaluating and enhancing the clinicalcapabilities of LLMs in specific domains, using osteoarthritis (OA) managementas a case study. A domain specific benchmark framework was developed, whichevaluate LLMs across a spectrum from domain-specific knowledge to clinicalapplications in real-world clinical scenarios. DocOA, a specialized LLMtailored for OA management that integrates retrieval-augmented generation (RAG)and instruction prompts, was developed. The study compared the performance ofGPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and humanevaluations. Results showed that general LLMs like GPT-3.5 and GPT-4 were lesseffective in the specialized domain of OA management, particularly in providingpersonalized treatment recommendations. However, DocOA showed significantimprovements. This study introduces a novel benchmark framework which assessesthe domain-specific abilities of LLMs in multiple aspects, highlights thelimitations of generalized LLMs in clinical contexts, and demonstrates thepotential of tailored approaches for developing domain-specific medical LLMs.

Evaluating and Enhancing Large Language Models Performance in Domain-specific Medicine: Osteoarthritis Management with DocOA