Explainable AI Methods and Applications

Explainable Artificial Intelligence (XAI) is transforming artificial intelligence by boosting end-user trust in technology. The literature still lacks a well-organized and comprehensive survey on the use of XAI in healthcare. We are attempting to address this need by emphasizing the capabilities of XAI frameworks in healthcare to achieve accountability, transparency, result tracing, and model improvement in the healthcare sector, infecting a wide range of fields and disciplines of research. Some of them call for a high standard of transparency and responsibility in the medical industry. Therefore, machine choices and predictions need explanations to support their veracity. Greater interpretability is necessary for this, which frequently requires knowledge of the algorithms underlying mechanisms. In this chapter, we present the well-known XAI methods, services, and applications. The technique in XAI is discussed as being used to analyze and diagnose health data utilizing AI-based technologies. As a result, we may gain a better understanding of how the healthcare industry functions as a whole by studying how diverse inputs interact. The various categories show diverse aspects of interpretability research, ranging from methods that produce information that is “obviously” interpretable to investigations of complex patterns. It is hoped that by categorizing interpretability in medical research similarly to other types of research, (1) clinicians and practitioners will be able to approach these techniques with caution, (2) new understandings of interpretability will emerge with greater consideration for medical practices, and (3) initiatives to promote data-driven, mathematically and technically sound medical education will be supported.