Explainable AI in Healthcare: Building Trust in AI-Powered Diagnosis

Abstract: The healthcare industry is experiencing a transformation because of artificial intelligence, which delivers both powerful diagnosis and prognosis and treatment design capabilities. Opacity in many AI models creates concerns about clinical decision-making transparency while also threatening trust in medical decision systems as well as ethical standards. Such issues gain particular importance in critical fields, including oncology, together with mental health treatment and personalized medical practices. The emergence of explainable AI (XAI) represents a fundamental solution to these problems by giving healthcare professionals understandable insights that show how AI systems operate. This work examines why healthcare needs XAI solutions through an explanation of various explainable methods while addressing the human-focused ethical and legal barriers to implementation. Explainable technology serves as a basic requirement to build trust because it exists as both a technological need and a social requirement and a legal essential and clinical necessity. The successful adoption of XAI into clinical settings requires proper regulatory oversight while using interdisciplinary teamwork and continuous staff training because it ensures accountable, equitable applications of AI in healthcare.