Full Description
Healthcare is fundamentally different from other domains where AI has achieved remarkable success. When an AI system recommends a treatment, suggests a diagnosis, or flags a patient for intervention, lives hang in the balance. Healthcare professionals require more than accurate predictions; they need to understand the reasoning behind those predictions. Explainable AI (XAI) provides the transparency necessary to identify and address algorithmic biases that might perpetuate or exacerbate health disparities.
This book addresses this critical challenge by exploring the intersection of healthcare informatics and XAI. It brings together diverse perspectives from clinicians, data scientists, ethicists, and healthcare administrators to examine how transparent and interpretable AI systems can enhance medical practice while maintaining the trust and confidence of both healthcare providers and patients. The book not only showcases technological capabilities but also demonstrates how explainability can bridge the gap between AI innovation and clinical reality.
Maintaining a balance between technical rigor and practical accessibility, the book presents detailed discussions of explainability techniques including SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-agnostic Explanations), and causal inference methods. Case studies and examples demonstrate how different XAI techniques can be selected and tailored based on specific requirements. The book also addresses critical implementation challenges.
At the threshold of AI's deeper integration into healthcare, the choices made today about transparency and explainability will shape the future of medicine. This book argues that explainability is not a luxury or an afterthought—it is a fundamental requirement for responsible AI deployment in healthcare.
Contents
1. AI Meets Nussbaum: Ethics for Smarter Healthcare 2. AI-Driven Decision-Making in Clinical Settings 3. Decision-Making Transparency Beyond Clinical Settings: Explainable AI Innovation in Telehealth and the Role of Social and Developmental Factors in AI Adoption 4. Explainable Multimodal Systems in Electronic Health Records and Predictive Analytics 5. Personalization in Healthcare Using AI 6. A Systematic Review of EEG Analysis for the Identification and Classification of Harmful Brain Activity 7. AI for Health Monitoring and Wearable Devices 8. Explainable AI for Mental Health in Healthcare Workers 9. Artificial Intelligence in India's Healthcare Revolution: Transforming Diagnostics and Personalized Care through Collaboration 10. Artificial Intelligence for Health Monitoring and Wearable Devices 11. AI for Mental Health Care: Applications, Promise, Pitfalls, and Strategies to Build Patient and Health‑Worker Trust
