TY - JOUR
T1 - Wearable and Mobile Sensing in Clinical Care Optimization: Validation Frameworks, Drift Detection, and Generalization Challenges
AU - Luis Fernandez
AU - Marta Ruiz
JF - Journal of Health Informatics and Digital Systems
JO - J. Health Inform. Digit. Syst.
SN - 3149-8973
Y1 - 2026
VL - 6
IS - 2
SP - 62
N2 - 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.
UR - https://cirpublications.com/h986083334
ER -