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The full acceptance of Deep Learning (DL) models in the clinical field israther low with respect to the quantity of high-performing solutions reportedin the literature. Particularly, end users are reluctant to rely on the roughpredictions of DL models. Uncertainty quantification methods have been proposedin the literature as a potential response to reduce the rough decision providedby the DL black box and thus increase the interpretability and theacceptability of the result by the final user. In this review, we propose anoverview of the existing methods to quantify uncertainty associated to DLpredictions. We focus on applications to medical image analysis, which presentspecific challenges due to the high dimensionality of images and their qualityvariability, as well as constraints associated to real-life clinical routine.We then discuss the evaluation protocols to validate the relevance ofuncertainty estimates. Finally, we highlight the open challenges of uncertaintyquantification in the medical field.

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