ABSTRACT
In present medical decisions, the use of artificial intelligence (AI) systems is based on trust and transparency. Explainable AI (XAI) is an emerging field that happens to throw light on “black box” machine learning (ML) models in human understandable language. XAI is a solution to several scientific problems that need deep explanation and interpretation. XAI solutions with quantitative and qualitative analyses have proved the efficiency for many clinical problems. This chapter focuses on few algorithms of XAI, namely ANFIS-GA, LIME, GRAD-CAM, and SIDU. In this study, these algorithms were implemented on various cases such as heart attack prediction, eye fundus, thyroid, COVID-19, and air pollutant.
