Wearable and mobile sensing technologies are transforming healthcare by enabling continuous monitoring, real-time analytics, and personalized interventions. This narrative review explores recent advances in artificial intelligence (AI)–driven healthcare analytics, focusing on validation frameworks, drift detection, and generalization challenges associated with wearable sensing systems. Modern wearable devices equipped with biosensors capture physiological signals such as heart rate, activity, and stress indicators, while AI algorithms analyze multimodal data to generate actionable clinical insights. Ensuring reliability requires robust validation strategies that address sensor accuracy, data integrity, and clinical relevance in real-world settings. Drift detection methods help maintain model performance despite environmental changes and user variability. At the same time, generalization techniques support reliable deployment across diverse populations and clinical contexts, advancing scalable and adaptive digital healthcare systems.
This systematic review examines the use of edge artificial intelligence (AI) and wearable sensors for real-time patient monitoring in smart hospitals and home settings, focusing on detecting deterioration, falls, arrhythmias, and infection-related changes. The review synthesizes studies from 2017 to 2026 on edge AI architectures, wearable sensor fusion, and clinical alert systems, emphasizing latency, power constraints, alert performance, and integration into clinical workflows. A PRISMA 2020-compliant search identified 127 studies from 2,100 records, with findings showing that while edge AI execution grew post-2020, it still represented a minority of designs. Sensor fusion was often linked to broader event coverage but increased implementation complexity. The review concludes that edge AI can reduce latency and enhance privacy but introduces challenges related to power usage, model complexity, device reliability, and maintenance, with limited clinical validation of alert systems and few studies addressing alert fatigue or clinician response.
Clinical Intelligence Research Press