Explainable AI in Clinical Practice : Methods, Applications, and Implementation

個数：

予約

Explainable AI in Clinical Practice : Methods, Applications, and Implementation

Panwar, Arvind (EDT)/ Jain, Achin (EDT)/ Mallik, Saurav (EDT)

ウェブストア価格 ¥37,452（本体¥34,048）
Academic Press Inc（2026/04発売）
外貨定価 US$ 175.00
ポイント 340pt

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製本 Paperback:紙装版/ペーパーバック版／ページ数 440 p.
言語 ENG
商品コード 9780443441110

Full Description

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.

Section I: Foundations
1. Foundations of AI in Healthcare
2. Introduction to XAI in Healthcare
3. Understanding the Need for Transparency in Clinical AI
4. Theoretical Frameworks for XAI in Medicine
5. AI Bias and Fairness in Clinical Applications
6. Evaluation Frameworks for Healthcare XAI

Section II: Methods and Technologies
7. XAI Techniques for Medical Image Analysis
8. Natural Language Processing in Clinical Documentation
9. Time Series Analysis for Patient Monitoring
10. Integration of Multiple Data Modalities

Section III: Clinical Applications
11. XAI in Diagnostic Support Systems
12. Transparent AI for Treatment Planning
13. Risk Prediction and Preventive Care
14. Drug Discovery and Development
15. Performance Metrics and Quality Assurance
16. Integration with Clinical Workflows

Section IV: Ethical and Regulatory Considerations
17. Ethics of Transparent AI in Healthcare
18. Privacy and Security Considerations
19. Regulatory Compliance and Standards
20. Patient Trust and Acceptance

Section V: Future Directions
21. Emerging Trends and Technologies
22. Challenges and Opportunities
23. Future Research Directions