Wearable biosensors and artificial intelligence (AI) are transforming modern healthcare by enabling continuous, real-time, and personalized health monitoring. Wearable biosensors can continuously measure physiological and biochemical parameters such as heart rate, body temperature, blood oxygen saturation, glucose levels, and physical activity, while AI techniques process these large and complex datasets to generate meaningful clinical insights. This review examines recent developments in wearable biosensor technologies, AI-based health data analysis, and their integration for personalized healthcare. It discusses the classification of wearable biosensors, the application of machine learning and deep learning algorithms, and the expanding clinical use of AI-enabled wearable devices in cardiovascular care, chronic disease management, remote patient monitoring, and preventive medicine. The review also highlights major challenges related to sensor accuracy, interoperability, ethical concerns, data privacy, cybersecurity, and regulatory issues that may influence the successful implementation of these technologies. Furthermore, future trends, including intelligent health ecosystems, multimodal sensing platforms, digital twins, and precision medicine, are explored. Overall, the integration of wearable biosensors and artificial intelligence has the potential to improve diagnostic accuracy, support early disease detection, enhance patient engagement, optimize clinical decision-making, and facilitate the transition toward proactive, patient-centered, and personalized healthcare systems.

Wearable Biosensors; Artificial Intelligence; Personalized Health Monitoring; Digital Health; Precision Medicine.