Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Jan 7, 2025
Open Peer Review Period: Jan 7, 2025 - Mar 4, 2025
Date Accepted: May 12, 2025
(closed for review but you can still tweet)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Artificial Intelligence (AI) in Health Promotion and Disease Reduction: A Rapid Review
ABSTRACT
Background:
Chronic diseases represent a significant global burden of mortality, exacerbated by behavioral risk factors. Artificial Intelligence (AI) has transformed health promotion and disease reduction through improved early detection, encouraging healthy lifestyle modifications, and mitigating the economic strain on health systems.
Objective:
To investigate how AI contributes to health promotion and disease reduction among Organization for Economic Co-operation and Development (OECD) countries.
Methods:
We conducted a rapid review of the literature to identify the latest evidence on how AI is used in health promotion and disease reduction. We applied comprehensive search strategies formulated for Medline (OVID) and CINAHL to locate studies published within 2019-2024. A pair of reviewers independently applied the inclusion and exclusion criteria to screen the titles and abstracts, assess the full texts, and extract the data. We synthesized extracted data from the study characteristics, intervention characteristics, and intervention purpose using structured narrative summaries of main themes, giving a portrait of the current scope of available AI initiatives used in promoting healthy activities and preventing disease.
Results:
We included 22 studies in this review, out of 3442 publications screened, most of which were conducted in the USA (10/22, 45%), and focused on health promotion by targeting lifestyle dimensions, such as dietary behavior (10/22, 45%), smoking cessation (6/22; 27%), physical activity (4/22, 18%), and mental health (3/22, 14%). Three studies targeted disease reduction related to metabolic health (eg, obesity, diabetes, hypertension). Most AI initiatives were AI-powered mobile applications. Overall, positive results were reported for process outcomes (eg, acceptability, engagement), cognitive and behavioral outcomes (eg, confidence, step count), and health outcomes (eg, glycemia, blood pressure). We categorized the challenges, benefits, and suggestions identified in the studies using a Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis to inform future developments.
Conclusions:
These findings offer critical insights into the effective implementation of AI for health promotion and disease prevention, potentially guiding policymakers and healthcare practitioners in optimizing the use of AI technologies in supporting health promotion and disease reduction. Clinical Trial: Open Science Framework (OSF) Registry e9v6x; https://osf.io/e9v6x/
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.