Position Paper: Integrating Explainability and Uncertainty Estimation in Medical AI

• URL: http://arxiv.org/abs/2509.18132v1
• Date: Sun, 14 Sep 2025 02:41:26 GMT
• Title: Position Paper: Integrating Explainability and Uncertainty Estimation in Medical AI
• Authors: Xiuyi Fan,
• Abstract summary: We propose Explainable Uncertainty Estimation (XUE) to enhance trust and usability in medical AI.<n>We map medical uncertainty to AI uncertainty concepts and identify key challenges in implementing XUE.<n>This work contributes to the development of trustworthy medical AI by bridging explainability and uncertainty.
• Score: 1.692908842436229
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Uncertainty is a fundamental challenge in medical practice, but current medical AI systems fail to explicitly quantify or communicate uncertainty in a way that aligns with clinical reasoning. Existing XAI works focus on interpreting model predictions but do not capture the confidence or reliability of these predictions. Conversely, uncertainty estimation (UE) techniques provide confidence measures but lack intuitive explanations. The disconnect between these two areas limits AI adoption in medicine. To address this gap, we propose Explainable Uncertainty Estimation (XUE) that integrates explainability with uncertainty quantification to enhance trust and usability in medical AI. We systematically map medical uncertainty to AI uncertainty concepts and identify key challenges in implementing XUE. We outline technical directions for advancing XUE, including multimodal uncertainty quantification, model-agnostic visualization techniques, and uncertainty-aware decision support systems. Lastly, we propose guiding principles to ensure effective XUE realisation. Our analysis highlights the need for AI systems that not only generate reliable predictions but also articulate confidence levels in a clinically meaningful way. This work contributes to the development of trustworthy medical AI by bridging explainability and uncertainty, paving the way for AI systems that are aligned with real-world clinical complexities.

Related papers

• Bridging AI and Clinical Reasoning: Abductive Explanations for Alignment on Critical Symptoms [5.220940151628734]
  Key challenge is that AI reasoning diverges from structured clinical frameworks.<n>We leverage formal abductive explanations, which offer consistent, guaranteed reasoning.<n>This enables a clear understanding of AI decision-making and allows alignment with clinical reasoning.
  arXiv  Detail & Related papers  (2026-02-15T04:27:59Z)
• Agentic Uncertainty Quantification [76.94013626702183]
  We propose a unified Dual-Process Agentic UQ (AUQ) framework that transforms verbalized uncertainty into active, bi-directional control signals.<n>Our architecture comprises two complementary mechanisms: System 1 (Uncertainty-Aware Memory, UAM), which implicitly propagates verbalized confidence and semantic explanations to prevent blind decision-making; and System 2 (Uncertainty-Aware Reflection, UAR), which utilizes these explanations as rational cues to trigger targeted inference-time resolution only when necessary.
  arXiv  Detail & Related papers  (2026-01-22T07:16:26Z)
• Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation [97.36081721024728]
  We propose the first benchmark for assessing confidence in multi-turn interaction during realistic medical consultations.<n>Our benchmark unifies three types of medical data for open-ended diagnostic generation.<n>We present MedConf, an evidence-grounded linguistic self-assessment framework.
  arXiv  Detail & Related papers  (2026-01-22T04:51:39Z)
• AI Deception: Risks, Dynamics, and Controls [153.71048309527225]
  This project provides a comprehensive and up-to-date overview of the AI deception field.<n>We identify a formal definition of AI deception, grounded in signaling theory from studies of animal deception.<n>We organize the landscape of AI deception research as a deception cycle, consisting of two key components: deception emergence and deception treatment.
  arXiv  Detail & Related papers  (2025-11-27T16:56:04Z)
• The challenge of uncertainty quantification of large language models in medicine [0.0]
  This study investigates uncertainty quantification in large language models (LLMs) for medical applications.<n>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.
  arXiv  Detail & Related papers  (2025-04-07T17:24:11Z)
• Explaining Uncertainty in Multiple Sclerosis Lesion Segmentation Beyond Prediction Errors [30.086986374579414]
  This study introduces a novel framework to explain the potential sources of predictive uncertainty.<n>The proposed analysis shifts the focus from the uncertainty-error relationship towards relevant medical and engineering factors.<n>Our findings reveal that instance-wise uncertainty is strongly related to lesion size, shape, and cortical involvement.
  arXiv  Detail & Related papers  (2025-04-07T08:09:27Z)
• Uncertainty-aware abstention in medical diagnosis based on medical texts [87.88110503208016]
  This study addresses the critical issue of reliability for AI-assisted medical diagnosis.<n>We focus on the selection prediction approach that allows the diagnosis system to abstain from providing the decision if it is not confident in the diagnosis.<n>We introduce HUQ-2, a new state-of-the-art method for enhancing reliability in selective prediction tasks.
  arXiv  Detail & Related papers  (2025-02-25T10:15:21Z)
• Artificial Intelligence-Driven Clinical Decision Support Systems [5.010570270212569]
  The chapter emphasizes that creating trustworthy AI systems in healthcare requires careful consideration of fairness, explainability, and privacy.<n>The challenge of ensuring equitable healthcare delivery through AI is stressed, discussing methods to identify and mitigate bias in clinical predictive models.<n>The discussion advances in an analysis of privacy vulnerabilities in medical AI systems, from data leakage in deep learning models to sophisticated attacks against model explanations.
  arXiv  Detail & Related papers  (2025-01-16T16:17:39Z)
• Probabilistic Modeling of Disparity Uncertainty for Robust and Efficient Stereo Matching [61.73532883992135]
  We propose a new uncertainty-aware stereo matching framework.<n>We adopt Bayes risk as the measurement of uncertainty and use it to separately estimate data and model uncertainty.
  arXiv  Detail & Related papers  (2024-12-24T23:28:20Z)
• Certainly Uncertain: A Benchmark and Metric for Multimodal Epistemic and Aleatoric Awareness [106.52630978891054]
  We present a taxonomy of uncertainty specific to vision-language AI systems. We also introduce a new metric confidence-weighted accuracy, that is well correlated with both accuracy and calibration error.
  arXiv  Detail & Related papers  (2024-07-02T04:23:54Z)
• Uncertainty Quantification for Forward and Inverse Problems of PDEs via Latent Global Evolution [110.99891169486366]
  We propose a method that integrates efficient and precise uncertainty quantification into a deep learning-based surrogate model. Our method endows deep learning-based surrogate models with robust and efficient uncertainty quantification capabilities for both forward and inverse problems. Our method excels at propagating uncertainty over extended auto-regressive rollouts, making it suitable for scenarios involving long-term predictions.
  arXiv  Detail & Related papers  (2024-02-13T11:22:59Z)
• Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities [2.9404725327650767]
  Review of progress in developing explainable models for clinical risk prediction. emphasizes the need for external validation and the combination of diverse interpretability methods. End-to-end approach to explainability in clinical risk prediction is essential for success.
  arXiv  Detail & Related papers  (2023-08-16T14:51:51Z)
• A Review of Uncertainty Estimation and its Application in Medical Imaging [32.860577735207094]
  Uncertainty estimation plays a pivotal role in producing a confidence evaluation along with the prediction of the deep model. This is particularly important in medical imaging, where the uncertainty in the model's predictions can be used to identify areas of concern or to provide additional information to the clinician.
  arXiv  Detail & Related papers  (2023-02-16T06:54:33Z)
• Clinical Outcome Prediction from Admission Notes using Self-Supervised Knowledge Integration [55.88616573143478]
  Outcome prediction from clinical text can prevent doctors from overlooking possible risks. Diagnoses at discharge, procedures performed, in-hospital mortality and length-of-stay prediction are four common outcome prediction targets. We propose clinical outcome pre-training to integrate knowledge about patient outcomes from multiple public sources.
  arXiv  Detail & Related papers  (2021-02-08T10:26:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.