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The challenge of uncertainty quantification of large language models in medicine
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This study investigates uncertainty quantification in large language models (LLMs) for medical applications, emphasizing both technical innovations and philosophical implications. As LLMs become integral to clinical decision-making, accurately communicating uncertainty is crucial for ensuring reliable, safe, and ethical AI-assisted healthcare. Our research frames uncertainty not as a barrier but as an essential part of knowledge that invites a dynamic and reflective approach to AI design. By integrating advanced probabilistic methods such as Bayesian inference, deep ensembles, and Monte Carlo dropout with linguistic analysis that computes predictive and semantic entropy, we propose a comprehensive framework that manages both epistemic and aleatoric uncertainties. The framework incorporates surrogate modeling to address limitations of proprietary APIs, multi-source data integration for better context, and dynamic calibration via continual and meta-learning. Explainability is embedded through uncertainty maps and confidence metrics to support user trust and clinical interpretability. Our approach supports transparent and ethical decision-making aligned with Responsible and Reflective AI principles. Philosophically, we advocate accepting controlled ambiguity instead of striving for absolute predictability, recognizing the inherent provisionality of medical knowledge.
Forward citations
Cited by 6 Pith papers
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Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
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LEC proposes a +1-corrected threshold for FDR control in selective prediction and routing, but the finite-sample guarantee rests on a false exchangeability identity and is not valid for arbitrary exchangeable data.
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Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances
Across the 68 papers it surveys, domain-specialized generative models usually outperform general-purpose LLMs on biological tasks, and agentic/conversational workflows are the least-covered topics.
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Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation
A virtue-labeled lookup table maps coarse uncertainty tags to canned warnings or disclaimers; the only quantitative result is 50% tag accuracy on 20 author-written prompts, with trust improvements asserted but untested.
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