The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes. It discusses the integration of explainable large language models (LLMs) into electronic health records (EHRs) and clinical workflows to facilitate data analysis, improve documentation, and support care delivery. Challenges such as privacy concerns, data complexity, and adapting models to specific domains are addressed, alongside evaluation techniques for explainability, including metrics, benchmarks, and human-centered assessments to ensure AI explanations are accurate and clinically relevant. Ethical considerations like fairness, accountability, and balancing transparency with patient confidentiality are highlighted, with case studies and empirical evidence illustrating the benefits and challenges of safely implementing XAI in clinical settings.
The book explores real-world applications of explainable AI (XAI) safely in healthcare, focusing on disease diagnosis, treatment planning, and patient management, while demonstrating how XAI enhances clinical decision-making and patient outcomes.
1. Introduction to AI Safety in Healthcare Systems
2. Ethical and Legal Considerations in AI Safety
3. Data Privacy and Security in AI-Enabled Healthcare
4. Risk Management and Safety Protocols for AI Systems
5. AI Model Transparency in Healthcare
6. Detection and Prevention of AI Failures and Anomalies
7. Patient Safety in AI-Driven Healthcare
8. Future Directions and Challenges in AI Safety for Healthcare
Azadeh Zamanifar, Head of AI/IT Department, Science and research branch, Islamic Azad University, Tehran, Iran.
Amir Taherkordi, Professor of Computer Science, University of Oslo, Norway.
Amirfarhad Farhadi, Postdoctoral Fellow, Iran University of Science and Technology, Tehran, Iran.
