翻訳待ち：Large Language Models Show Metacognitive Sensitivity in Medical Reasoning
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。ソース概要：arXiv:2608.14552v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical benchmark to test diagnostic choice and confidence behavior in a medical LLM. The benchmark focused on probable Alzheimer-type neurocognitive disorder (AT-NCD) versus depression-related cognitive impairment (DRCI). We generated 45 synthetic vignettes varying evidence strength, conflicting evidence, and missing information. Each vignette was presented under three prompt variants, yielding 135 trials. In a pilot run with gpt-4.1-nano, all trials produced valid structured outputs. Across forced-choice trials, diagnostic accuracy was 93.5%, mean confidence was 78.4%, and AUROC2 was 0.876. Confidence increased with evidence distance from the diagnostic boundary, decreased when information was missing, and remained higher on correct than incorrect trials after adjustment for evidence strength and prompt format. These findings indicate partial metacognitive sensitivity rather than globally uninformative confidence. However, errors clustered in moderate, conflicting AT-NCD cases, where the model shifted toward DRCI and retained more confidence than empirical accuracy justified. Model comparison suggested that confidence quality should be measured directly rather than inferred from benchmark accuracy or model capability alone. This study establishes a reproducible framework for evaluating evidence sensitivity, metacognitive sensitivity, and localized calibration failure in medical LLMs.
AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
--> [Submitted on 4 May 2026] Title:Large Language Models Show Metacognitive Sensitivity in Medical Reasoning View a PDF of the paper titled Large Language Models Show Metacognitive Sensitivity in Medical Reasoning, by Ahmad Nazzal View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical benchmark to test diagnostic choice and confidence behavior in a medical LLM. The benchmark focused on probable Alzheimer-type neurocognitive disorder (AT-NCD) versus depression-related cognitive impairment (DRCI). We generated 45 synthetic vignettes varying evidence strength, conflicting evidence, and missing information. Each vignette was presented under three prompt variants, yielding 135 trials. In a pilot run with gpt-4.1-nano, all trials produced valid structured outputs. Across forced-choice trials, diagnostic accuracy was 93.5%, mean confidence was 78.4%, and AUROC2 was 0.876. Confidence increased with evidence distance from the diagnostic boundary, decreased when information was missing, and remained higher on correct than incorrect trials after adjustment for evidence strength and prompt format. These findings indicate partial metacognitive sensitivity rather than globally uninformative confidence. However, errors clustered in moderate, conflicting AT-NCD cases, where the model shifted toward DRCI and retained more confidence than empirical accuracy justified. Model comparison suggested that confidence quality should be measured directly rather than inferred from benchmark accuracy or model capability alone. This study establishes a reproducible framework for evaluating evidence sensitivity, metacognitive sensitivity, and localized calibration failure in medical LLMs. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14552 [cs.AI] (or arXiv:2608.14552v1 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2608.14552 arXiv-issued DOI via DataCite Submission history From: Ahmad Nazzal [view email] [v1] Mon, 4 May 2026 06:25:21 UTC (1,647 KB) Full-text links: Access Paper: View a PDF of the paper titled Large Language Models Show Metacognitive Sensitivity in Medical Reasoning, by Ahmad Nazzal View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI new | recent | 2026-08 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)