ArticleNature communications2025
Large Language Models lack essential metacognition for reliable medical reasoning.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
52 citing papers in PubMed.
- Embracing Digital as a Paradigm Shift in Medical Affairs.Pharmaceutical medicine · 2026Review
- The Rise of Small Language Models in Healthcare: A Comprehensive Survey.Computer science review · 2026Article
- Towards an integrative neuroscience of metacognition.Nature reviews. Neuroscience · 2026Review
- Performance, Failures, and Oversight of a Large Language Model Agent for Clinical Data Analysis: Evaluation Study.Journal of medical Internet research · 2026Article
- Large Language Models in Preclinical Spine Research: A Scoping Review and Expert Perspective on Evidence-Aware Experimental Workflows.JOR spine · 2026Article
- Benchmarking large language models for question answering on German clinical practice guidelines.BMJ health & care informatics · 2026Article
- Review
- Mitigating hallucinations in synthesized clinical texts to improve multimodal deep learning for dermatology.Journal of biomedical informatics · 2026Article
- Tutorial: guidance on the use of large language models for medical research.Nature protocols · 2026Review
- Med-CoReasoner: Reducing Language Disparities in Medical Reasoning via Language-Informed Co-Reasoning.Proceedings of the conference. Association for Computational Linguistics. Meeting · 2026Article
- Memorization in large language models in medicine prevalence characteristics and implications.Nature communications · 2026Article
- Does ChatGPT need a psychiatrist? Similarities between human psychopathology and errors in large language models.NPP - digital psychiatry and neuroscience · 2026Review
- AI-induced never-skilling in medical education.Nature medicine · 2026Review
- Augmenting large language models with clinical knowledge graph for personalized perioperative fluid therapy question answering.PLOS digital health · 2026Article
- Adaptive yet suboptimal integration of advice in decision-making.Communications psychology · 2026Article
- An Entity-Based Visual Analytics System Enhancing Medical Expertise Acquisition: Development and Verification Study.JMIR medical informatics · 2026Article
- Blocking the Reflection: Milestones and Hurdles for Digital Twins in Mental Health.Pharmacopsychiatry · 2026Review
- St. Gallen International Breast Cancer Consensus-Based Clinical Decision Validation: Concordance Assessment Between Deep Large Language Model Outputs and Global Expert Panel Recommendations.Annals of surgical oncology · 2026Article
- The Performance of DeepSeek R1 and Gemini 3 in Complex Medical Scenarios: Comparative Study.JMIRx med · 2026Article
- Large Language Model Performance and Clinical Reasoning Tasks.JAMA network open · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large Language Models have demonstrated expert-level accuracy on medical board examinations, suggesting potential for clinical decision support systems. However, their metacognitive abilities, crucial for medical decision-making, remain largely unexplored. To address this gap, we developed MetaMedQA, a benchmark incorporating confidence scores and metacognitive tasks into multiple-choice medical questions. We evaluated twelve models on dimensions including confidence-based accuracy, missing answer recall, and unknown recall. Despite high accuracy on multiple-choice questions, our study revealed significant metacognitive deficiencies across all tested models. Models consistently failed to recognize their knowledge limitations and provided confident answers even when correct options were absent. In this work, we show that current models exhibit a critical disconnect between perceived and actual capabilities in medical reasoning, posing significant risks in clinical settings. Our findings emphasize the need for more robust evaluation frameworks that incorporate metacognitive abilities, essential for developing reliable Large Language Model enhanced clinical decision support systems.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.