Explainable AI (XAI) in healthcare

Explainable artificial intelligence in healthcare refers to AI systems that provide transparent, interpretable explanations for their decisions and recommendations in medical contexts.

Unlike black-box AI models, XAI systems enable healthcare professionals to understand the reasoning behind AI-generated diagnoses, treatment recommendations, and risk assessments. This transparency is crucial for building trust between clinicians and AI systems, ensuring regulatory compliance, and maintaining accountability in medical decision-making. XAI techniques include attention mechanisms that highlight relevant features in medical images, decision trees that show logical pathways, and natural language explanations that describe the AI's reasoning process. In healthcare applications, XAI helps clinicians validate AI recommendations, identify potential biases, and make informed decisions about patient care.

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Regulatory bodies increasingly require explainability for AI systems used in clinical settings, making XAI essential for the adoption of AI in healthcare. The integration of XAI with clinical workflows enhances physician confidence and improves patient safety by providing clear rationales for AI-assisted medical decisions