Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866916837443239936 |
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| author | Felde, Sabine Buchkremer, Rüdiger Chehab, Gamal Thielscher, Christian Distler, Jörg HW Schneider, Matthias Richter, Jutta G. |
| author_facet | Felde, Sabine Buchkremer, Rüdiger Chehab, Gamal Thielscher, Christian Distler, Jörg HW Schneider, Matthias Richter, Jutta G. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_07983 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology Felde, Sabine Buchkremer, Rüdiger Chehab, Gamal Thielscher, Christian Distler, Jörg HW Schneider, Matthias Richter, Jutta G. Computation and Language Artificial Intelligence L01.224.900.500 (Primary), L01.700.508.300, L01.224.050.375, H02.403.720.750, N04.590, N04.452.758.625 (Secondary) I.2.7; H.3.3; J.3; I.2.9; C.4 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. |
| title | Performance and Practical Considerations of Large and Small Language Models in Clinical Decision Support in Rheumatology |
| topic | Computation and Language Artificial Intelligence L01.224.900.500 (Primary), L01.700.508.300, L01.224.050.375, H02.403.720.750, N04.590, N04.452.758.625 (Secondary) I.2.7; H.3.3; J.3; I.2.9; C.4 |
| url | https://arxiv.org/abs/2507.07983 |