Real‐World Applications of Explainable AI in Healthcare

Abstract

Artificial intelligence (AI) has completely restructured the medical field by offering predictive analytics, customized therapies, and improved diagnostics. However, trust, accountability, and productive teamwork are all hampered by the opaque nature of artificial intelligence algorithms. One important option that stands out is Explainable Artificial Intelligence (XAI), which emphasizes interpretability and transparency in AI-driven decision-making processes. This study thoroughly examines the practical uses, methods, and consequences of explainable artificial intelligence (XAI) in healthcare. It sheds light on how explainable AI improves clinical decision support systems, drug development, personalized medicine, diagnostic imaging, and patient outcome prediction using empirical data and case studies. It also discusses the ethical problems and issues, such as algorithmic transparency, privacy issues, and biases. This paper also highlights the reforming potential of explainable AI in healthcare through an analysis of present practices and future prospects along with advocacy for multidisciplinary cooperation and ethical frameworks to enable responsible execution. In the era of artificial intelligence-driven medicine, explainable artificial intelligence (XAI) has definitely emerged as a catalyst for improving healthcare practices, promoting transparency, confidence, and patient-centered treatment.

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