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Explainable Artificial Intelligence (XAI) is becoming increasingly critical in diagnostic systems, particularly in healthcare, where transparency and trust are paramount. XAI aims to make AI systems more interpretable by providing clear insights into how decisions are made, improving trustworthiness and regulatory compliance in medical diagnostics. This paper explores the role of XAI in diagnostic systems, outlining key methodologies, current applications, challenges, and potential future directions. Case studies are presented to demonstrate how explainable AI is integrated into diagnostic workflows, enhancing transparency and facilitating more informed decision-making by healthcare professionals.
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SHREYA GAUTAM WAGHMARE - (2024) studied this question.
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