Evaluating and Enhancing Large Language Models’ Performance in Domain-Specific Medicine: Development and Usability Study With DocOA
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Abstract
Background: The efficacy of large language models (LLMs) in domain-specific medicine, particularly for managing complex diseases such as osteoarthritis (OA), remains largely unexplored. Objective: This study focused on evaluating and enhancing the clinical capabilities and explainability of LLMs in specific domains, using OA management as a case study. Methods: A domain-specific benchmark framework was developed to evaluate LLMs across a spectrum from domain-specific knowledge to clinical applications in real-world clinical scenarios. DocOA, a specialized LLM designed for OA management integrating retrieval-augmented generation and instructional prompts, was developed. It can identify the clinical evidence upon which its answers are based through retrieval-augmented generation, thereby demonstrating the explainability of those answers. The study compared the performance of GPT-3.5, GPT-4, and a specialized assistant, DocOA, using objective and human evaluations. Results: Results showed that general LLMs such as GPT-3.5 and GPT-4 were less effective in the specialized domain of OA management, particularly in providing personalized treatment recommendations. However, DocOA showed significant improvements. Conclusions: This study introduces a novel benchmark framework that assesses the domain-specific abilities of LLMs in multiple aspects, highlights the limitations of generalized LLMs in clinical contexts, and demonstrates the potential of tailored approaches for developing domain-specific medical LLMs.
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Prior research indicates that although agents can assume specific roles, their fundamental abilities may be insufficient in the domain-specific knowledge and skills required to function optimally as clinical specialists in complex clinical tasks.
“This observation is consistent with prior research indicating that although agents can assume specific roles, their fundamental abilities may be insufficient in the domain-specific knowledge and skills require to function optimally as clinical specialists in complex clinical task26,27.”
From Enhancing diagnostic capability with multi-agents conversational large language models (2025) · cites this paper as [27]
In the review, twenty studies relied exclusively on closed-source models.
“In our review, twenty studies relied exclusively on closed-source models [62,63,79,89,105,118]”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [63]
Some explanations cite specific, retrievable evidence, e.g. RAG-grounded source citations or knowledge-graph paths, enabling independent verification.
“Some explanations cite specific, retrievable evidence, e.g. RAG-grounded source citations or knowledge-graph paths, enabling independent verification . This characterizes the RAG-grounded approaches in our review [63, 87,91,92,94,99].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [63]
Retrieval-Augmented Generation can, in principle, ground explanations on retrieved sources, enabling verification against clinical guidelines.
“RetrievalAugmented Generation (RAG) [26] can, in principle, ground explanations on retrieved sources, enabling verification against clinical guidelines [63,94].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [63]
Integration with external knowledge sources has emerged as a strategy to ground model outputs in verifiable medical knowledge.
“Integration with external knowledge sources, including clinical guidelines [63] and medical knowledge graphs [111], has emerged as a strategy to ground model outputs in verifiable medical knowledge.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [63]
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Self-citation
- Authors
- Chen, X., Wang, L., You, M., Liu, W., Fu, Y., Xu, J., Zhang, S., Chen, G., Li, K., & Li, J.
- Affiliations
- Sichuan University (CN)
- Shanghai Artificial Intelligence Laboratory (CN)
- Corresponding author
- Li, Jian
- Venue
- Journal of Medical Internet Research
- Publisher
- JMIR Publications Inc.
- Published
- 2024-07-22
- Volume
- 26
- Pages
- e58158
- ISSN
- 1438-8871 (ISSN-L)
- PMC
- PMC11301122
- OpenAlex
- W4399327441
- Cited by
- 48
- References
- 26
- Citations by year
- 2026: 24 · 2025: 21 · 2024: 3
- FWCI
- 4.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of Medical Internet Research (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- large language models, osteoarthritis management, retrieval-augmented generation, explainability, personalized treatment recommendations, LLM benchmarking, GPT-4, GPT-3.5, clinical decision support, instructional prompts
- MeSH terms
- Machine Learning, Humans, Osteoarthritis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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