REAL-TIME PATIENT MONITORING IN SMART MEDICAL BEDS USING AI AND EMBEDDED SENSORS

Abstract

Smart medical beds are emerging as an important platform for continuous patient monitoring in hospitals, rehabilitation units, and long-term care settings. By combining embedded sensors with artificial intelligence (AI), these systems can detect movement patterns, bed-exit events, physiological changes, and early indicators of clinical deterioration in real time. This article examines the technical architecture, clinical value, implementation challenges, and privacy considerations of AI-enabled sensor systems in smart medical beds. The review further argues that interoperability, privacy-preserving design, and clinically reliable alert systems are essential for safe and scalable deployment in healthcare environments. The paper also discusses how AI-enabled infrastructure systems may contribute to improved patient safety, reduced workload on healthcare personnel, and modernization of healthcare delivery systems.