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Wearable technologies are driving a paradigm shift in chronic disease management from clinics to patients 'everyday environments. Their applications are diverse: early arrhythmia detection in cardiology, optimal glycemic control in diabetology, remote monitoring in pulmonology, and opening new paradigms for objective assessment in neurology and medication adherence. This constant stream of data drives proactive, personalized & data-driven healthcare. Many of those advances are still limiting widespread clinical integration. Technical challenges regarding data validation and algorithmic transparency remain, alongside complex ethical questions about data privacy and the risk of algorithmic bias. Also, unresolved economic issues like reimbursement models threaten to increase health inequalities The future of digital health will depend on integrating wearable data with artificial intelligence. It will unlock predictive analytics to forecast disease exacerbations and enable the development of reliable digital biomarkers within the vision of P4 medicine - predictive, preventive, personalized, and participatory. This narrative review critically summarizes the available data, identifies these ethical and practical obstacles, and suggests ways to safely, successfully, and fairly incorporate wearable technology into standard medical procedures.
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Mierniczek et al. (2025) studied this question.
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