Explainable Artificial Intelligence (XAI) Approaches in Healthcare Diagnostics and Analysis

Abstract

Explainable Artificial Intelligence (XAI) has emerged as a critical field for enabling trustworthy, transparent, and clinically acceptable AI-driven diagnostic systems. While deep learning and complex ensemble models have achieved high performance on many diagnostic tasks (image interpretation, disease risk prediction, time-series monitoring), their “black-box” nature creates barriers for clinical adoption due to safety, accountability, and regulatory requirements. This paper reviews major XAI approaches used in healthcare diagnostics, presents a taxonomy of methods (intrinsically interpretable models, post-hoc explanation techniques, model-agnostic vs. model-specific approaches), surveys typical applications and case studies, discusses datasets and evaluation metrics for explanations, outlines methodological and practical challenges (stability, fidelity, evaluation, human factors), and highlights ethical, legal, and regulatory considerations. We conclude with future research directions emphasizing hybrid human–AI workflows, standardized evaluation frameworks, and techniques to reconcile explain ability with high performance in safety-critical clinical contexts.