Explainable AI in Medicine: A Comprehensive Narrative Review of Methods, Applications, and Future Directions

ABSTRACT Artificial Intelligence (AI) is increasingly utilised in medicine; however, its “black‐box” nature continues to hinder clinical trust, adoption, and validation. Explainable AI (XAI) has emerged as a critical field to address these transparency challenges by making AI‐driven decisions more interpretable and actionable. This narrative review examines the progress of XAI in medicine over the past decade. We first introduce fundamental XAI concepts and describe our review methodology, followed by a comprehensive analysis of key application domains, including medical imaging, electronic health records (EHRs), and multi‐omics data. Methodologically, we categorise XAI techniques into model‐agnostic approaches (e.g., SHAP, LIME, Anchors) and model‐specific approaches (e.g., Grad‐CAM, LRP, TreeSHAP). Beyond summarising their principles, advantages, and limitations, we further provide a systematic analysis of clinical reliability and failure modes associated with each class of methods, highlighting how explanation techniques may produce misleading, unstable, or non‐causal interpretations in real‐world clinical settings. The review then discusses the demonstrated benefits of XAI, including result validation, bias detection, and improved patient–clinician communication, while critically examining persistent challenges such as limited clinical deployment, inconsistent evaluation standards, and the lack of prospective validation. Finally, we outline future research directions, emphasising the need to adapt XAI to large‐scale foundation models and conversational AI systems, as well as to extend its applicability in biomedical and multi‐omics interpretation. We argue that while XAI is essential for improving transparency, trust, and clinical adoption, its reliable and scalable integration into clinical workflows requires both methodological innovation and rigorous, clinically grounded validation frameworks.