# The challenge of uncertainty quantification of large language models in medicine

arXiv: https://arxiv.org/abs/2504.05278 · HTML: https://mlsanity.com/paper/2504.05278/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 117 references that are in the map.
- First posted on arXiv 7 Apr 2025.

## Builds on (most cited first, 30 of 34)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Advances and Open Problems in Federated Learning](https://mlsanity.com/paper/1912.04977/) (2019)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift](https://mlsanity.com/paper/1906.02530/) (2019)
- [Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs](https://mlsanity.com/paper/2306.13063/) (2023)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020)
- [A Survey of Uncertainty in Deep Neural Networks](https://mlsanity.com/paper/2107.03342/) (2021)
- [Selective Question Answering under Domain Shift](https://mlsanity.com/paper/2006.09462/) (2020)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Evaluating Language Models for Mathematics through Interactions](https://mlsanity.com/paper/2306.01694/) (2023)
- [Unremarkable AI: Fitting Intelligent Decision Support into Critical, Clinical Decision-Making Processes](https://mlsanity.com/paper/1904.09612/) (2019)
- [Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation](https://mlsanity.com/paper/2311.08877/) (2023)
- [Selective Classification Can Magnify Disparities Across Groups](https://mlsanity.com/paper/2010.14134/) (2020)
- [Quantifying Uncertainty in Natural Language Explanations of Large Language Models](https://mlsanity.com/paper/2311.03533/) (2023)
- [Deep Generative Model-based Quality Control for Cardiac MRI Segmentation](https://mlsanity.com/paper/2006.13379/) (2020)
- [Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series](https://mlsanity.com/paper/2010.11310/) (2020)
- [Uncertainty Estimation and Sample Selection for Crowd Counting](https://mlsanity.com/paper/2009.14411/) (2020)
- [Uncertainty-Aware Explainable Recommendation with Large Language Models](https://mlsanity.com/paper/2402.03366/) (2024)
- [The Four Dimensions of Social Network Analysis: An Overview of Research Methods, Applications, and Software Tools](https://mlsanity.com/paper/2002.09485/) (2020)
- [Explainable Natural Language Processing for Corporate Sustainability Analysis](https://mlsanity.com/paper/2407.17487/) (2024)
- [Enhancing chest X-ray datasets with privacy-preserving large language models and multi-type annotations: a data-driven approach for improved classification](https://mlsanity.com/paper/2403.04024/) (2024)
- [Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation](https://mlsanity.com/paper/2311.08931/) (2023)
- [Selective prediction-set models with coverage guarantees](https://mlsanity.com/paper/1906.05473/) (2019)
- [Segmentation of Bruch's Membrane in retinal OCT with AMD using anatomical priors and uncertainty quantification](https://mlsanity.com/paper/2210.14799/) (2022)
- [Statistical learning theory and Occam's razor: The core argument](https://mlsanity.com/paper/2312.13842/) (2023)
- [Reflective Artificial Intelligence](https://mlsanity.com/paper/2301.10823/) (2023)

## Built on by (most cited first, 14 of 14)

- [LEC: Linear Expectation Constraints for Selection-Conditioned Risk Control in Selective Prediction and Routing Systems](https://mlsanity.com/paper/2512.01556/) (2025)
- [Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol](https://mlsanity.com/paper/2508.20737/) (2025)
- [Mind the Ambiguity: Aleatoric Uncertainty Quantification in LLMs for Safe Medical Question Answering](https://mlsanity.com/paper/2601.17284/) (2026)
- [Mind the Gap: Benchmarking LLM Uncertainty and Calibration with Specialty-Aware Clinical QA and Reasoning-Based Behavioural Features](https://mlsanity.com/paper/2506.10769/) (2025)
- [Rule-Based Moral Principles for Explaining Uncertainty in Natural Language Generation](https://mlsanity.com/paper/2509.07190/) (2025)
- [Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems](https://mlsanity.com/paper/2602.02582/) (2026)
- [Aligning Clinical Needs and AI Capabilities: A Survey on LLMs for Medical Reasoning](https://mlsanity.com/paper/2607.07761/) (2026)
- [Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs](https://mlsanity.com/paper/2506.00072/) (2025)
- [ScenarioBench: Trace-Grounded Compliance Evaluation for Text-to-SQL and RAG](https://mlsanity.com/paper/2509.24212/) (2025)
- [Kantian-Utilitarian XAI: Meta-Explained](https://mlsanity.com/paper/2510.03892/) (2025)
- [Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances](https://mlsanity.com/paper/2511.03354/) (2025)
- [Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation](https://mlsanity.com/paper/2601.15645/) (2026)
- [MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction](https://mlsanity.com/paper/2606.25651/) (2026)
- [GUT: Quantifying and Optimizing the Reasoning Uncertainty of LLMs via Graph Complexity](https://mlsanity.com/paper/2609.05284/) (2026)

## Abstract (arXiv)

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.