The deep integration of artificial intelligence (AI) in healthcare has given rise to innovative paradigms such as precision diagnosis, personalized treatment, and proactive health. However, the opacity of AI decision⁃making (often termed the “black box” effect) can trigger clinical trust crises and monitor regulatory compliance challenges. Explainable artificial intelligence (XAI), by unveiling the logical framework behind model decisions, has emerged as a pivotal approach to reconciling the tension between AI's potential and its application bottlenecks. XAI is evolving from an auxiliary explanatory tool into a systematic solution. It is poised to shift medical AI from “outcome delivery” to “process transparency”, laying the foundation for a trustworthy intelligent healthcare ecosystem. This paper systematically deconstructs the multidimensional implications of interpretability in medical AI, traces the evolution of key technologies, including feature importance analysis, causal inference, and multi⁃modal fusion, and delves into core challenges such as data heterogeneity and accountability demarcation, supported by empirical studies in cutting⁃edge applications like medical imaging diagnosis and drug discovery. 

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