ArticleFrontiers in medicine2025
Evaluation of large language models in rheumatology and clinical immunology: a systematic assessment based on Chinese national health professional qualification examination.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Adapting transformer-based encoder models for automated billing code assignment of current procedural terminology from pathology reports in multi-institutional settings.Journal of pathology informatics · 2026Article
- Article
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8 authors.
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Abstract
In recent years, large language models (LLMs) have achieved remarkable progress in natural language processing and demonstrated potential applications in medicine. However, their professional capabilities in specific medical subfields, such as immunology, still require systematic evaluation. This study systematically evaluated 11 representative LLMs, including DeepSeek, GPT, Llama, Gemma, and Qwen series, based on the Chinese National Health Professional Qualification Examination in Rheumatology and Clinical Immunology. The evaluation covered four dimensions: basic medical knowledge, related medical knowledge, immunology knowledge, and professional practice ability. Results show significant differences among LLMs. DeepSeek-R1 and Qwen3 achieve the best performance, with accuracy exceeding 90%. However, performance on professional practice ability tasks remained relatively low, highlighting limitations in complex clinical applications.
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