Explainable Medical AI

Abstract

Scientific explanations form an integral part of robust scientific practice, offering avenues for understanding, advancing scientific knowledge, and informed decision-making. This chapter delves into explanatory AI (artificial intelligence) (XAI), emphasizing the role of scientific explanations in the application of machine learning in the medical and healthcare domains. After offering a brief overview of the merits of employing AI in the fields of medicine and healthcare, as well as highlighting the value of XAI for physicians, patients, and institutions, this chapter establishes a core distinction between how-explanations and why-explanations. This distinction is essential for obtaining a deeper understanding of the complexities surrounding the XAI debate. This chapter also highlights that while how-explanations have received significant attention within more technically inclined literature, including philosophical discourse, why-explanations have somewhat lagged in exploration. This is unfortunate, since explaining why an algorithm suggests a given treatment recommends a give drug dosage or determines that a mole is malignant and carries enormous epistemic and moral value. Despite this, this chapter concludes optimistically, highlighting the increasing prominence of why-explanations in the ongoing discussions on XAI. Overall, this chapter illuminates the dynamic landscape of AI in the fields of medicine and healthcare, underscoring the significance and indispensability of XAI, alongside the challenges that lie ahead.