Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. As AI systems become prevalent in clinical decision-making, transparency and explainability are crucial. This volume unites leading experts to provide a comprehensive framework for implementing explainable AI, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound. The book explores applications of explainable AI in diagnostic support and treatment planning, offering insights into making AI systems interpretable and accountable. Through real-world case studies and ethical frameworks, readers learn to transform opaque AI systems into tools that enhance clinical practice while maintaining high patient care standards. Targeted solutions cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals gain confidence in integrating AI tools, while technical teams receive implementation guidelines. This book is essential for anyone seeking to navigate the complexities of AI in healthcare responsibly and effectively.