Intelligent Health Systems: AI-Driven Solutions for Patient Care and Diagnosis

Hospitals and clinics now generate more data than any clinician can read. Bedside monitors, wearable sensors, electronic health records, imaging systems and laboratory databases produce continuous streams of information, and the practical question is no longer how to collect it but how to turn it into attention directed at the right patient at the right moment. This volume examines what artificial intelligence and machine learning can contribute to that problem, and where their limits currently lie. The chapters gathered here approach intelligent health systems from the engineering side as well as the clinical one. They cover AI-enabled central monitoring stations for hospital-wide patient handling, supervised and deep learning models for heart disease prediction, machine learning-compatible optical fiber sensors for physiological measurement, and head-wearable assistive technology for people with visual impairment. Across these topics a consistent argument emerges. These systems are most valuable when they support clinical judgement rather than substitute for it, when their data pipelines and validation strategies are described honestly, and when questions of privacy, bias, resource constraint and real-world deployment are treated as central rather than as afterthoughts. The contributors report measured results alongside candid accounts of what remains unresolved. The volume is written for research scholars, postgraduate students, biomedical and electronics engineers, clinicians and healthcare administrators who want a grounded view of where AI-driven patient care and diagnosis have actually reached.