Explainable AI for Healthcare
Real Life Applications and Use Cases for Practitioners
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Product details:
- Edition number 1
- Publisher Chapman and Hall
- Date of Publication 4 December 2025
- ISBN 9781032911120
- Binding Hardback
- No. of pages196 pages
- Size 234x156 mm
- Language English
- Illustrations 22 Illustrations, black & white; 22 Line drawings, black & white; 19 Tables, black & white 700
Categories
Short description:
This book explores the transformative potential of Explainable AI (XAI) in enhancing healthcare delivery and XAI's role in fostering transparency, trust, and accountability in AI-driven medical decision-making.
MoreLong description:
This book explores the transformative potential of Explainable AI (XAI) in enhancing healthcare delivery and XAI's role in fostering transparency, trust, and accountability in AI-driven medical decision-making. Covering technical foundations, practical applications, and ethical considerations, it offers valuable insights into how XAI can improve clinical decision-making, patient outcomes, and healthcare operations. Through real-world case studies, the book illustrates the practical benefits of XAI in diverse healthcare scenarios. It also addresses the challenges and solutions related to deploying XAI, making it an essential resource for professionals and researchers.
• Detailed exploration of the methodologies, algorithms, and regulatory considerations underpinning XAI in smart healthcare systems
• Diverse case studies demonstrating practical applications and benefits of XAI across various healthcare domains, enhancing understanding through tangible examples
• Exploration of innovative XAI applications in diagnosis, treatment, patient monitoring, and care delivery, showcasing its potential to revolutionize healthcare practices and improve outcomes
• Discussion on how XAI promotes patient engagement by providing clear explanations of AI-driven diagnoses or treatment plans, enhancing patient understanding and participation in their healthcare
• Breakdown of XAI techniques, algorithms, and interpretability strategies, helping medical professionals understand and trust AI-driven decision-making processes
MoreTable of Contents:
1 Introduction to Explainable AI in Smart Healthcare Systems 2. Understanding the Framework of Explainable AI in Healthcare 3. The Role of Transparency and Interpretability in Healthcare AI 4. Ethical Considerations in Implementing Explainable AI in Healthcare 5. Advancing Healthcare with Explainable AI: Enhancing Patient Monitoring and Outcomes 6. Navigating Interpretability in AI: Balancing Performance, Standards, and Practical Guidance 7. Applications of Explainable AI in Diagnosis and Treatment 8. Challenges and Solutions in Deploying Explainable AI in Smart Healthcare Systems 9. Case Studies: Real-world Examples of Explainable AI in Healthcare 10. Future Directions and Innovations in Explainable AI for Healthcare
Appendix -A
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