When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information

Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks. However, their reliability under uncertainty remains poorly understood which raises critical concerns for deployment in high-stakes clinical settings. In such environments, incorrect predictions are inherently risky, but confident incorrect predictions can be particularly harmful as they may mislead clinical decision-making. In this paper, we conduct a systematic behavioral a

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ethics.ai (10 August 2026), “When Confidence Fails: Overconfidence in LLMs under Uncertainty and Missing Clinical Information,” evidence record 18281, https://ethics.ai/record/18281 (originally published by arXiv cs.HC).

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