<![CDATA[The advent of Explainable AI (XAI) in healthcare, often referred to as Healthcare 5.0, presents both significant opportunities and challenges. XAI promises to enhance clinical decision-making by providing transparent and interpretable insights into AI-driven diagnoses and treatment recommendations, thereby increasing trust and adoption among healthcare practitioners. This paper explores the evolving landscape of XAI in healthcare, highlighting its potential to improve patient outcomes, reduce errors, and optimize resource allocation. However, it also addresses the challenges of implementing XAI, including data privacy concerns, regulatory hurdles, and the need for robust validation methods. Balancing these opportunities and challenges is critical for realizing the full potential of XAI in revolutionizing healthcare delivery.]]>
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