mlsanityData to 14 Oct 2026

Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis

arXiv 2210.03736 ↗First posted 5 Oct 2022eess.IV cs.AI cs.CVcited by 63

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Abstract (arXiv)

The full acceptance of Deep Learning (DL) models in the clinical field is rather low with respect to the quantity of high-performing solutions reported in the literature. Particularly, end users are reluctant to rely on the rough predictions of DL models. Uncertainty quantification methods have been proposed in the literature as a potential response to reduce the rough decision provided by the DL black box and thus increase the interpretability and the acceptability of the result by the final user. In this review, we propose an overview of the existing methods to quantify uncertainty associated to DL predictions. We focus on applications to medical image analysis, which present specific challenges due to the high dimensionality of images and their quality variability, as well as constraints associated to real-life clinical routine. We then discuss the evaluation protocols to validate the relevance of uncertainty estimates. Finally, we highlight the open challenges of uncertainty quantification in the medical field.

Key facts. Cited by 63 papers in the map (data through 14 Oct 2026). Builds on 143 of its 311 references that are in the map. First posted on arXiv 5 Oct 2022.

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143 of 311 references.

References · 143

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Citing papers per year: 2023: 4; 2024: 13; 2025: 23; 2026: 23.

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  1. 2025Conformal Prediction for Image Segmentation Using Morphological Prediction Setscited by 14
  2. 2024Comparative Benchmarking of Failure Detection Methods in Medical Image Segmentation: Unveiling the Role of Confidence Aggregationcited by 12
  3. 2023Empirical Validation of Conformal Prediction for Trustworthy Skin Lesions Classificationcited by 11
  4. 2023Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentationcited by 7
  5. 2025Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge resultscited by 7
  6. 2024A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Datacited by 6
  7. 2025Generalizable and Explainable Deep Learning for Medical Image Computing: An Overviewcited by 6
  8. 2024BayTTA: Uncertainty-aware medical image classification with optimized test-time augmentation using Bayesian model averagingcited by 5
  9. 2023Identifying Drivers of Predictive Aleatoric Uncertaintycited by 4
  10. 2024A Comprehensive Survey and Classification of Evaluation Criteria for Trustworthy Artificial Intelligencecited by 3
  11. 2024Denoising Diffusion Probabilistic Models for Magnetic Resonance Fingerprintingcited by 3
  12. 2024Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Modelscited by 3
38 more
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  2. 2025CONSIGN: Conformal Segmentation Informed by Spatial Groupings via Decompositioncited by 3
  3. 2025KonfAI: A Modular and Fully Configurable Framework for Deep Learning in Medical Imagingcited by 3
  4. 2025Calibration-Aware Prompt Learning for Medical Vision-Language Modelscited by 3
  5. 2025Uncertainty Reasoning with Photonic Bayesian Machinescited by 3
  6. 2024Deep Evidential Learning for Radiotherapy Dose Predictioncited by 2
  7. 2024pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularizationcited by 2
  8. 2024OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class positive-only annotationscited by 2
  9. 2025Federated Conditional Conformal Prediction via Generative Modelscited by 2
  10. 2025SingleStrip: learning skull-stripping from a single labeled examplecited by 1
  11. 2025A Probabilistic Segment Anything Model for Ambiguity-Aware Medical Image Segmentationcited by 1
  12. 2025Uncovering Overconfident Failures in CXR Models via Augmentation-Sensitivity Risk Scoringcited by 1
  13. 2025Autonomous Uncertainty Quantification for Computational Point-of-care Sensorscited by 1
  14. 2026Better than Average: Spatially-Aware Aggregation of Segmentation Uncertainty Improves Downstream Performancecited by 1
  15. 2026Reliability-Aware CT-MRI Registration: A Quality Engineering Framework with Stability Analysis and Risk Classificationcited by 1
  16. 2023Bias-Reduced Neural Networks for Parameter Estimation in Quantitative MRIcited by 0
  17. 2024Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentationcited by 0
  18. 2024Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantificationcited by 0
  19. 2024Multi-Stage Segmentation and Cascade Classification Methods for Improving Cardiac MRI Analysiscited by 0
  20. 2024Uncertainty quantification for improving radiomic-based models in radiation pneumonitis predictioncited by 0
  21. 2025Weakly Supervised Pixel-Level Annotation with Visual Interpretabilitycited by 0
  22. 2025Explaining Uncertainty in Multiple Sclerosis Cortical Lesion Segmentation Beyond Prediction Errorscited by 0
  23. 2025AquaSignal: An Integrated Framework for Robust Underwater Acoustic Analysiscited by 0
  24. 2025Label tree semantic losses for rich multi-class medical image segmentationcited by 0
  25. 2025A Comprehensive Framework for Uncertainty Quantification of Voxel-wise Supervised Models in IVIM MRIcited by 0
  26. 2025Uncertainty-Aware Retinal Vessel Segmentation via Ensemble Distillationcited by 0
  27. 2025Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learningcited by 0
  28. 2025Influence of Classification Task and Distribution Shift Type on OOD Detection in Fetal Ultrasoundcited by 0
  29. 2025Enhancing Conformal Prediction via Class Similaritycited by 0
  30. 2025UbiQVision: Quantifying Uncertainty in XAI for Image Recognitioncited by 0
  31. 2026Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service marketscited by 0
  32. 2026Leveraging Multi-Rater Annotations to Calibrate Object Detectors in Microscopy Imagingcited by 0
  33. 2026Score-based diffusion models for severely ill-posed problems in diffuse optical tomographycited by 0
  34. 2026Towards Calibrating Prompt Tuning of Vision-Language Modelscited by 0
  35. 2026Function-Space Empirical Bayes Regularisation with Student's t Priorscited by 0
  36. 2026Human Knowledge Integrated Multi-modal Learning for Single Source Domain Generalizationcited by 0
  37. 2026Low-Field Magnetic Resonance Image Quality Enhancement using Undersampled k-Space and Out-of-Distribution Generalisationcited by 0
  38. 2026Low-Field Magnetic Resonance Image Enhancement using Undersampled k-Spacecited by 0

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