Large Language Models Show Metacognitive Sensitivity in Medical Reasoning

A new arXiv study (2608.14552v1) found that OpenAI's gpt-4.1-nano model achieved 93.5% diagnostic accuracy and 78.4% mean confidence across 135 trials in a controlled medical benchmark, with confidence tracking evidence quality and uncertainty, indicating partial metacognitive sensitivity. However, errors clustered in moderate, conflicting Alzheimer-type neurocognitive disorder cases, where the model retained more confidence than accuracy justified, highlighting localized calibration failure.

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.