Enhancing Healthcare Informatics with Transparent and Explainable AI

Healthcare is fundamentally different from other domains where AI has achieved remarkable success.When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance.Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions.Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities. This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI.It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients.The book not only showcases technological capabilities but also demonstrates how explainability ca