F. Molinari

TL;DR: In this paper , the authors compared the performance of different sleep disorder detection methods, involving differ-ent datasets or signals, and found eight sleep disorders, of which sleep apnea and insomnia were the most studied.

TL;DR: Multi-modality approaches involve fusing and analyzing various data types, including medical images, bio-signals, clinical records, to gain a more comprehensive understanding of patients ’ conditions.

TL;DR: In all the modalities covered by this review, image harmonization improved AI performance, with increasing of up to 24.42 % in classification accuracy and 47 % in segmentation Dice scores.

Abstract: BACKGROUND The increasing use of Deep Learning (DL) in healthcare has highlighted the critical need for improved transparency and interpretability. While Explainable Artificial Intelligence (XAI) methods provide insights into model predictions, reliability cannot be guaranteed by simply relying on explanations. OBJECTIVES This position paper proposes the integration of Uncertainty Quantification (UQ) with XAI methods to improve model reliability and trustworthiness in healthcare applications. METHODS We examine state-of-the-art XAI and UQ techniques, discuss implementation challenges, and suggest solutions to combine UQ with XAI methods. We propose a framework for estimating both aleatoric and epistemic uncertainty in the XAI context, providing illustrative examples of their potential application. RESULTS Our analysis indicates that integrating UQ with XAI could significantly enhance the reliability of DL models in practice. This approach has the potential to reduce interpretation biases and over-reliance, leading to more cautious and conscious use of AI in healthcare.