Advancing explainable AI in healthcare methods, applications, and ethical implications

Abstract

The introduction of Explainable Artificial Intelligence (XAI) in the application of healthcare Artificial Intelligence (AI), in which transparency and trust are crucial, has become a fundamental part of it. With the growing influence of AI systems on patient treatment, diagnosis, treatment recommendations, and risk prediction, it is indispensable to ensure that the results derived from these systems can be understood by both healthcare professionals and patients. This chapter examines the need for XAI in healthcare, focusing on its role in creating trust, accountability, and fairness. The chapter also investigates the different options among XAI like Local Interpretable Model-Agnostic Explanations (LIME), SHapley Additive exPlanations(SHAP), Integrated Gradients, and Concept Activation Vectors and their respective values of the outcomes of healthcare practices are also given. Real-life wellness exams serve as a way to practice the successful application of Explainable AI in the fields of radiology, individualized medicine, and emergency care. Perfectly understandable ways as well as daunting difficulties are equally on display. Among the ethical issues, the authors unanimously confer on the following key themes: bias mitigation, transparency, and patient autonomy, and the need of an organic alliance between practitioners, data scientists, ethicists, and regulators. XAI, as a part of the clinical workflow process, goes on to see the development of a more human-friendly, ethical AI that is used in healthcare and would, as a result, enhance the outcomes of patients and the decisions made by clinicians.

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