AI-Driven Early Detection Systems for Chronic Illnesses Using Wearable Health Data

Abstract

Chronic diseases such as diabetes, cardiovascular conditions, and respiratory illnesses are leading causes of morbidity and mortality worldwide (Fu et al., 2025). Early detection and intervention are critical to improving outcomes, and recent advances in wearable technology and artificial intelligence (AI) offer new pathways for proactive health monitoring. This paper provides a comprehensive review of AI-driven early detection systems that leverage data from wearable devices (e.g., smartwatches, fitness bands, smart patches) to identify early signs of chronic illnesses. Wearable sensors can continuously capture physiological metrics including heart rate variability, blood oxygen saturation (SpO₂), electrocardiogram (ECG) readings, sleep patterns, and physical activity. AI techniques – particularly machine learning (ML) and deep learning (DL) – can analyze these large, multi-dimensional data streams to detect subtle patterns associated with disease onset or exacerbation. We examine current literature and real-world case studies demonstrating successful early detection: for example, detecting atrial fibrillation via smartwatch ECG, predicting incipient diabetes from heart rate patterns, and identifying respiratory infections like COVID-19 through changes in breathing rate (Miller et al., 2020; Perez et al., 2019). Methodological innovations such as on-device edge AI, federated learning for privacy-preserving model training, and multimodal data integration are discussed as key enablers of these systems. We also address challenges – including data privacy, bias, accuracy, and clinical integration – that must be managed to translate these technological capabilities into practice. Finally, we outline future directions, emphasizing the need for robust regulatory frameworks, integration of wearable-derived data into electronic health records (EHRs), and continued research to improve predictive accuracy and equity. The tone throughout is formal and academic, positioning these developments in the context of peer-reviewed healthcare research.