Bringing Explainability to Deep Learning–Based Clinical Support Decision Systems

Abstract

Background: Different deep learning models have been developed to support clinical decision making. However, despite promising results, because of the black box nature of neural networks in general, it is difficult for humans to understand how the various parameters within the models are combined to produce an appropriate classification result. Therefore, reluctance to artificial intelligence based clinical decision support models remains high on patient as well as on physician side. Hence, explainability may become a critical feature for clinical decision support systems to increase user acceptance and may become a legal necessity in near feature.

Ähnliche Arbeiten