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arXiv · 2507.07983

Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology

Sabine Felde·Rüdiger Buchkremer·Gamal Chehab·Christian Thielscher·Jörg HW Distler·Matthias Schneider·Jutta G. Richter
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Abstract

Large language models (LLMs) show promise for supporting clinical decision-making in complex fields such as rheumatology. Our evaluation shows that smaller language models (SLMs), combined with retrieval-augmented generation (RAG), achieve higher diagnostic and therapeutic performance than larger models, while requiring substantially less energy and enabling cost-efficient, local deployment. These features are attractive for resource-limited healthcare. However, expert oversight remains essential, as no model consistently reached specialist-level accuracy in rheumatology.

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Sabine Felde, Rüdiger Buchkremer, Gamal Chehab, Christian Thielscher, Jörg HW Distler, Matthias Schneider, Jutta G. Richter. 2025-07-10. Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology. https://arxiv.org/abs/2507.07983

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