Explainable AI in Clinical Practice: Methods, Applications, and Implementation bridges the gap between artificial intelligence capabilities and their practical implementation in healthcare. 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. 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. Targeted solutions in the book cater to diverse stakeholders in the healthcare AI ecosystem. Healthcare professionals will gain confidence in integrating AI tools, while technical teams will receive implementation guidelines. This book is essential for anyone seeking to responsibly and effectively navigate the complexities of AI in healthcare.
