The challenge of uncertainty quantification of large language models in medicine

2025-04-07

AI Summary

This work addresses the critical challenge of insufficient uncertainty quantification in medical large language models (LLMs), which poses significant risks to clinical decision-making. We propose the first unified framework integrating both epistemic and aleatoric uncertainty estimation. Methodologically, we introduce uncertainty mapping and dynamic meta-calibration, synergistically combining Bayesian inference, deep ensembles, Monte Carlo Dropout, and semantic entropy computation to achieve high-fidelity, real-time uncertainty estimation and visualization. Departing from conventional deterministic outputs, our approach embraces “controllable fuzziness” — a philosophy aligned with the inherent tentativeness of medical knowledge and ethical imperatives. Empirical evaluation demonstrates substantial improvements in clinicians’ trust and adoption rates of AI-generated recommendations. The framework exhibits robustness across multi-source data fusion and continual learning settings. By enabling transparent, calibrated, and context-aware uncertainty awareness, it provides foundational technical support for deploying responsible and reflective AI in clinical practice.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Uncertainty Representations
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Quantifying uncertainty in medical LLMs for reliable AI healthcare
• Integrating probabilistic methods to manage epistemic and aleatoric uncertainties
• Promoting ethical AI via explainable uncertainty maps and confidence metrics

Innovation

• Integrates Bayesian inference and deep ensembles
• Uses surrogate modeling for proprietary API limitations
• Embeds explainability via uncertainty maps