mlsanityData to 14 Oct 2026

Uncertainty Quantification for Machine Learning in Healthcare: A Survey

arXiv 2505.02874 ↗First posted 4 May 2025cs.LG cs.AIcited by 14

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Abstract (arXiv)

Uncertainty Quantification (UQ) is pivotal in enhancing the robustness, reliability, and interpretability of Machine Learning (ML) systems for healthcare, optimizing resources and improving patient care. Despite the emergence of ML-based clinical decision support tools, the lack of principled quantification of uncertainty in ML models remains a major challenge. Current reviews have a narrow focus on analyzing the state-of-the-art UQ in specific healthcare domains without systematically evaluating method efficacy across different stages of model development, and despite a growing body of research, its implementation in healthcare applications remains limited. Therefore, in this survey, we provide a comprehensive analysis of current UQ in healthcare, offering an informed framework that highlights how different methods can be integrated into each stage of the ML pipeline including data processing, training and evaluation. We also highlight the most popular methods used in healthcare and novel approaches from other domains that hold potential for future adoption in the medical context. We expect this study will provide a clear overview of the challenges and opportunities of implementing UQ in the ML pipeline for healthcare, guiding researchers and practitioners in selecting suitable techniques to enhance the reliability, safety and trust from patients and clinicians on ML-driven healthcare solutions.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 148 of its 319 references that are in the map. First posted on arXiv 4 May 2025.

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148 of 319 references.

References · 148

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38 more
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  3. 2026Beyond Boundary Noise: Aggregated Aleatoric Uncertainty Fails to Capture Presence Ambiguity in 3D Lung Nodule Segmentationcited by 1
  4. 2025A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRIcited by 0
  5. 2025Bregman geometry-aware split Gibbs sampling for Bayesian Poisson inverse problemscited by 0
  6. 2026Data-Driven Priors for Uncertainty-Aware Risk Prediction of Clinical Deterioration using Multimodal Datacited by 0
  7. 2026ADAPTOOD: Uncertainty-Aware Fine-Tuning for Out-of-Distribution ECG Time Series Modelscited by 0
  8. 2026Hallucination in Medical Imaging AI: A Cross-Modality Analytical Framework for Taxonomy, Detection, and Mitigation under Regulatory Constraintscited by 0
  9. 2026Distribution-based deep multiple instance learning for tumor proportion scoring in NSCLCcited by 0
  10. 2026Integrating Neural Encoders in Bayesian Generalized Linear Mixed Models for Multimodal Datacited by 0
  11. 2026SafeImpute: Reliable Clinical Data Imputation via Conformal Selectioncited by 0
  12. 2026Conformal Prediction with Paraphrase-Aware Scoring for LLM Uncertainty Quantificationcited by 0
2 more
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  2. 2026Uncertainty Evaluation: Bridging Machine Learning and Measurement Sciencecited by 0