Handbook of AI-Based Models in Healthcare and Medicine 1st Edition

Handbook of AI-Based Models in Healthcare and Medicine, 1st Edition by Bhanu Chander, Koppala Guravaiah, B. Anoop, and G. Kumaravelan presents a compelling gateway into the practical application of artificial intelligence across clinical and public-health settings. Clear, contemporary, and action-oriented, this handbook immediately draws you into how AI is reshaping diagnosis, treatment planning, and health-system management.

Inside, readers will find approachable chapters on machine learning, deep learning, natural language processing, medical imaging, predictive analytics, and clinical decision-support systems. The authors balance theory with real-world examples, addressing data preprocessing, model validation, interpretability, ethics, privacy, and regulatory frameworks—essential knowledge for implementing AI responsibly across hospitals, clinics, telemedicine services, and community health programs around the world.

For clinicians, researchers, data scientists, and healthcare administrators, this volume translates complex algorithms into usable strategies that improve workflow efficiency, patient stratification, and diagnostic accuracy. It also explores deployment challenges and solutions tailored to diverse geographies, from high-resource urban centers to low-resource and rural environments, making it a useful resource for global health initiatives and local implementation alike.

Whether you’re building models, evaluating AI tools, or shaping policy, this handbook equips you with the insights and practical guidance needed to navigate the intersection of technology and medicine. Add this authoritative, user-friendly reference to your professional library and stay at the forefront of healthcare innovation—an essential read for anyone committed to evidence-based, ethically responsible AI in medicine.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.