The role of AI-integrated wearables in predictive healthcare: a scoping review

Background: Artificial intelligence (AI)-enabled wearables are innovative technologies in predictive healthcare which monitor people in real-time, detect diseases at early stages, and take corresponding actions by analyzing physiological signals of individuals. The objective of this study was to systematically review how AI, predictive analytics, and wearables are used to support early disease detection, more accurate diagnosis, continuous monitoring, and personalized treatment in various domains of healthcare. Methods: The article conducted a scoping review following PRISMA-ScR guidelines across six databases from 16 July 2025 to 19 July 2025. Peer-reviewed studies published between 1 January 2016 and 19 July 2025 on AI-enabled wearable technologies in predictive healthcare were searched in Google Scholar, PubMed, Scopus, Web of Science, and JSTOR. Two reviewers independently screened and extracted data using predefined criteria. Eligible studies focused on early diagnosis, continuous monitoring, chronic disease management, or personalized treatment. Methodological quality was mapped using the Joanna Briggs Institute (JBI) checklists. Data were extracted in Excel and synthesized narratively and thematically to identify trends, strengths, and research gaps. Results: A total of 51 studies were included in the qualitative synthesis, of which six were empirical studies (e.g., machine learning experiments, observational studies, or surveys) and 45 were reviews (systematic reviews, scoping reviews, narrative reviews, or meta-analyses). Together, the empirical studies represented more than 50 million participants across datasets, with individual sample sizes ranging from 7 to 4,036 participants. No meta-analysis was performed. Thematic synthesis revealed the following: (I) 31 studies (full and partial coverage) reported significant improvements in diagnostic accuracy for chronic and acute diseases; (II) 31 studies highlighted the role of AI-integrated wearables in chronic disease prediction and continuous remote monitoring; and (III) 36 studies emphasized integration challenges, including issues with data quality, proprietary algorithm transparency, interoperability with clinical workflows, and ethical and regulatory concerns. Conclusions: The evidence supports the potential of AI-integrated wearables in predictive and personalized healthcare but is limited by methodological variability and early-stage validation, reflecting a research field still transitioning from theoretical validation toward clinical implementation. Large-scale trials, standardization, and ethical approaches are required to realize the potential of this technology for widespread, equitable, clinically reliable use.