The challenge of uncertainty quantification of large language models in medicine

TL;DR

This paper addresses the critical challenge of uncertainty quantification in large language models (LLMs) for medical applications, arguing that communicating uncertainty is essential for safe, trustworthy AI-assisted care. It proposes a comprehensive framework that fuses Bayesian inference, deep ensembles, Monte Carlo dropout, and linguistic entropy with surrogate modeling, multi-source data integration, dynamic calibration, continual/meta-learning, and explainability via uncertainty maps and confidence metrics. Emphasizing Responsible and Reflective AI, the work integrates technical and philosophical perspectives to promote transparency, accountability, and ethical deployment in high-stakes clinical settings. The proposed approach aims to improve trust, safety, and interpretability of AI-driven medical decisions while accommodating proprietary API limitations and evolving medical knowledge.

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.

Figures (11)

• Figure 1: Various Drivers of Response Uncertainty. This diagram illustrates how user knowledge and biases, data quality (inputs and outputs), the AI system’s architecture, and context (such as local disease patterns or practice routines) interact to influence the level of uncertainty in model-generated responses. Each element contributes uniquely to overall system reliability, interpretability, and trustworthiness.
• Figure 2: Data as the Driver of Uncertainty: input quality, output quality, and integration. This diagram highlights the key factors influencing data uncertainty in AI systems. It shows how multimodal data integration, including structured data (e.g., Electronic Health Records) and unstructured data (e.g., images), along with biases introduced by system designed (system prompts and the decision chain) and user (noise, bias and mistakes in user prompts), impact the quality of inputs and the probabilistic nature of model outputs.
• Figure 3: Key Components of LLM Architecture for Uncertainty Quantification in Medicine. This diagram shows the primary factors influencing uncertainty in large language models (LLMs) used in healthcare. It highlights the relationship between epistemic uncertainty (due to limited understanding of model parameters) and aleatoric uncertainty (caused by inherent noise and ambiguity in medical data). The integration of domain-specific knowledge and advanced techniques in LLM architecture aims to manage and quantify these uncertainties, improving model reliability and interpretability in medical applications.
• Figure 4: User-Centric Factors Impacting Uncertainty Quantification in LLMs for medicine. This diagram illustrates the various factors that influence how users interact with large LLMs in medical contexts. It shows the impact of user-induced biases, diverse levels of user expertise, and the role of explainable AI (XAI) in providing clarity through textual annotations and confidence metrics. These elements collectively shape how uncertainty is perceived and managed in AI-assisted decision-making.
• Figure 5: Key Contextual Factors Influencing Uncertainty in LLMs for Medical Applications. This diagram highlights the primary contextual elements that affect uncertainty in medical AI systems. It shows how factors such as evolving medical guidelines, patient demographics, and local disease patterns influence uncertainty in model predictions. These elements are part of the broader medical task environment, which is characterized by dynamic, multi-agent, and partially observable properties.