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Large language models that \u201cthink\u201d before responding, so-called reasoning models, show promise to support such complex decision-making, yet their validity and reliability in this setting remain unclear. In our work, we present a comprehensive human evaluation of reasoning models for clinical reasoning. Our results indicate that state-of-the art reasoning models demonstrate sufficient test\u2013retest reliability and are competent or proficient in terms of their conceptual reasoning, completeness, correctness, relevance, and usefulness, with no statistically significant or clinically relevant differences between them. However, our qualitative analysis reveals weaknesses in logical coherence, patient-centeredness, empathy, and intuition, with most deviations from expert reasoning in the domain of intuition. Our findings underscore the importance of adopting a multidimensional framework for evaluating language model outputs and allow us to provide guidance for model selection and prompting strategies to enhance clinical reasoning performance.<\/jats:p>","DOI":"10.3389\/fdgth.2026.1789412","type":"journal-article","created":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T06:58:23Z","timestamp":1773817103000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Intelligence without intuition: a mixed-methods pilot study on reasoning models in musculoskeletal physiotherapy for low-back pain"],"prefix":"10.3389","volume":"8","author":[{"given":"Ricardo","family":"Knauer","sequence":"first","affiliation":[{"name":"KI-Werkstatt, University of Applied Sciences Berlin","place":["Berlin, 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