iot and ai for early diagnosis of chronic diseases: opportunities and challenges

Chronic conditions which include cardiovascular diseases, diabetes, chronic kidney disease, and chronic obstructive pulmonary disease (copd), contribute towards over 70% of deaths worldwide and continue to inflict unrelenting strain on health care systems. timely diagnosis remains the key factor in treatment efficacy, although traditional diagnostic approaches tend to be reactive in nature, episodic clinical visits, and manual interpretation frequently lead to late diagnosis. nevertheless, recent developments in the internet of things (iot) and artificial intelligence (ai) technologies offer venues through continuous monitoring; timely detection of anomalies; and individualized interventions. this review consolidates studies from thirty, peer-reviewed, high-quality articles from 2023–2025 on iot sensors, wearables, and edge-cloud systems with ai algorithms including machine learning, deep learning, and explainable ai. the findings presented substantial opportunities including real-time monitoring of physiological data; individualized diagnosis; remote-aided patient observation; more accurate diagnoses; and reduced healthcare costs. but these results were tempered by challenges including issues with privacy and data security; interoperability; limited energy and computational capacities in iot devices; ethical and regulatory concerns; and opacity of ai’s decision-making processes. some viable solutions including federated learning and blockchain-enabled secure sharing, tinyml for edge inferencing will emerge to address these challenges. as this review states, a responsible approach to integrating iot technologies, ai and related clinical practice can only be considered through interdisciplinary partnerships between clinicians, engineers and researchers, policy-makers, and ethicists. these partnerships can enable chronic disease care to shift away from a reactive paradigm towards more proactive, patient-centered care, and to avoid risk to both patients and healthcare systems if medical practice evolves without appropriate safeguards.