Book

Description

Artificial Intelligence (AI) is transforming healthcare, revolutionizing disease diagnostics, chronic disease management, and epidemic forecasting. This book explores how AI-powered methodologies, combined with the Internet of Things (IoT) and machine learning, are shaping the future of medicine and public health. This comprehensive volume presents three cutting-edge research studies that bridge AI, IoT, and Explainable AI (XAI) for smarter, data-driven healthcare solutions: - AI & IoT in Chronic Disease Management – Smart health monitoring systems have improved diabetes control (0.9% HbA1c reduction) and cardiovascular disease detection (25% improvement in arrhythmia prediction). AI-driven IoT remote monitoring also played a key role in reducing COVID-19 hospital admissions by 20%. - Explainable AI in Medical Diagnostics – By integrating SHAP, LIME, and Grad-CAM techniques, AI models in medical imaging have increased diagnostic accuracy by 15-20%, reducing false positives in diseases such as lung cancer and diabetic retinopathy. - AI for Climate-Based Disease Forecasting – Hybrid AI models combining climate and epidemiological data have achieved 85% accuracy in dengue outbreak prediction, improving outbreak response times by 30%. Beyond scientific innovation, this book critically examines the challenges of AI adoption in healthcare, including data privacy, algorithmic bias, regulatory barriers, and interoperability limitations. It provides actionable insights for researchers, clinicians, AI practitioners, and policymakers aiming to harness AI’s full potential while ensuring ethical and responsible deployment. Whether you are a healthcare professional, a data scientist, or a policymaker, this book offers valuable insights into AI’s role in reshaping modern medicine, enabling precision healthcare, and strengthening global health resilience.