Citation :

Raghvendra Narain Tripathi, Dr. Arun Deep Singh, 2025. "Explainable AI (XAI) for the High-Stakes Decision of Healthcare" International Journal of Computer Science & Information Technology  Volume 1, Issue 3: 1-11.

Abstract :

The application of Artificial Intelligence (AI) in healthcare has improved the diagnosis of diseases, forecasting outcomes and supported complex clinical decision making with intelligent systems. Yet the lack of transparency in a lot of AI models, especially those deep learning ones—which makes them hard to trust—presents an enormous safety and accountability problem, not least when lives are at stake. Now with the help of Explainable AI (XAI), we can fill this gap by providing an AI model that outputs explainable and interpretable results used for unbiased decision making. Abstract: The aim of this paper is to review the role of XAI in healthcare decision-making, investigate different interpretability methods reported so far and their impact on clinical research & practice with respect to real-world applications, and propose a framework to operationalize them into routine practice in the field of health care. We next present current challenges, regulatory issues and what research is needed in the future to develop AI that is not only effective but also morally appropriate in health.

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Keywords :

Explainable AI, Healthcare, Deep Learning, Interpretability, Clinical Decision Support, Medical Ethics, Black-Box Models.