Explainable AI in Clinical Practice

Methods, Applications, and Implementation

Arvind Panwar, Achin Jain, Saurav Mallik, Aimin Li, Korhan Cengiz

• Publisher: Academic Press
• Year: 2026
• Topic: Biological Sciences
• eBook ISBN:
  9780443441127
• Subtopic: Biology
• Index: Biological Sciences

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. - Provides a comprehensive framework for implementing explainable AI in healthcare, ensuring that AI-driven decisions are transparent, trustworthy, and clinically sound - Includes real-world case studies that illustrate practical applications of explainable AI - Offers targeted solutions for diverse stakeholders in the healthcare AI ecosystem

Table of contents

• Title of Book
• Chapter 1 Foundations of artificial intelligence in healthcare
• Chapter 2 Introduction to explainable artificial intelligence in healthcare
• Chapter 3 A comparative evaluation of machine learning and deep learning models across diverse datasets for early detection of lung cancer
• Chapter 4 Elucidable Artificial Intelligence in Medical Practice: Enhancing Transparency, Accountability, and Responsible Artificial Intelligence use
• Chapter 5 Explainable artificial intelligence for breast cancer diagnosis: a SHAP-driven approach
• Chapter 6 Explainable artificial intelligence techniques for image analysis
• Chapter 7 Explainable artificial intelligence in a diagnostic support system
• Chapter 8 Explainable artificial intelligence techniques for medical image analysis
• Chapter 9 Integration of multiple data modalities
• Chapter 10 Theoretical frameworks for explainable AI in medicine
• Chapter 11 Patient trust and acceptance of artificial intelligence in healthcare
• Chapter 12 Enhancing clinical transparency through exPlainable artificial intelligence in surface electromyograph–based lower limb activity recognition
• Chapter 13 Enhancing diagnostic accuracy: the role of explainable artificial intelligence in clinical decision support
• Chapter 14 Artificial intelligence bias and fairness in clinical applications
• Chapter 15 Explainable artificial intelligence for medicine prediction: interpretable deep learning and similarity models for therapeutic use analysis from chemical compositions
• Chapter 16 Early diagnosis of heart disease using machine learning and explainable artificial intelligence: a robust framework for improved healthcare outcomes
• Chapter 17 Risk prediction and preventive care using explainable artificial intelligence in clinical practice
• Chapter 18 Transparent artificial intelligence for treatment planning
• Chapter 19 A novel approach and solution based on natural language processing technique for diagnostic analysis and clinical documentation
• Chapter 20 Privacy and security challenges of explainable artificial intelligence in healthcare
• Chapter 21 Ethics of transparent artificial intelligence in health care
• Chapter 22 Emerging trends and technologies
• Chapter 23 The future of explainable AI in clinical decision-making: challenges and research directions
• Index