Exploring the Role of Artificial Intelligence in Enhancing Nursing Bed Equipment: A Scoping Review

Abstract

Artificial intelligence (AI) has been increasingly integrated into nursing bed equipment to enable continuous patient monitoring, reduce adverse events, and improve the quality of care for bedridden and elderly individuals. Smart nursing beds equipped with sensors and AI algorithms can non-invasively detect posture changes, falls, and physiological abnormalities; however, the scope, technological maturity, and limitations of these systems remain insufficiently synthesized in the existing literature. This study presents a scoping review of AI‑based nursing bed equipment, focusing on sensor technologies, application areas, and analytical methods. Four electronic databases—Web of Science, PubMed, IEEE Xplore, and CINAHL—were searched for studies published between January 2010 and July 2024. A total of 5,496 records were identified, and 4,184 unique articles remained after duplicate removal. Following screening in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines, 135 studies (3.23%) were included in the final analysis. Pressure sensors were the most frequently used sensing modality (43.0%), followed by RGB (Red Green–Blue) cameras (11.1%), infrared and thermal imaging sensors (8.9%), and depth cameras (7.4%), while other modalities—including accelerometers, radar, radio-frequency sensors, microphones, and multi-sensor systems—accounted for 37.0% of the studies. The primary application domains were in-bed posture classification and activity monitoring, followed by fall detection, physiological monitoring, and human–machine interaction, with deep learning methods, particularly convolutional and recurrent neural networks, being the most commonly employed analytical approaches. Overall, AI-based nursing bed equipment shows considerable potential to enhance patient safety and care efficiency; nevertheless, challenges related to deployment costs, data privacy, and limited clinical validation remain and must be addressed to enable large-scale adoption and real-world implementation of intelligent nursing bed systems.