Explainable Artificial Intelligence (XAI) in Healthcare

Abstract

In recent years, AI has emerged as a game-changing tool in the healthcare industry, with the potential to improve treatment, diagnosis, and study design significantly. However, questions about AI algorithms’ openness, interpretability, and trustworthiness in life-and-death healthcare contexts arise due to their complexity and “black box” nature. Explainable AI (XAI) has emerged as a critical solution to overcome these obstacles by revealing the logic underlying AI model decisions. Using artificial intelligence for healthcare requires openness, responsibility, and accountability. This abstract discusses the importance of XAI to promote those qualities. This chapter discusses a variety of XAI methods, such as feature importance, rule-based models, and local explanations, which help doctors interpret AI-generated diagnoses, treatment options, and predict outcomes. In the abstract, XAI is discussed as a tool for decision support, diagnostics, medication research, and patient monitoring in healthcare. Aside from that, it emphasizes the role of XAI in advancing ethical AI, reducing bias, and ensuring regulatory compliance in healthcare. In conclusion, XAI in the future will be discussed emphasizing interpretability, simplicity, and continuous learning to maximize synergy between AI and human knowledge in healthcare delivery.

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