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“Artificial Intelligence Applications To Personalized Dietary Recommendations: A Systematic Review” Metadata:

  • Title: ➤  Artificial Intelligence Applications To Personalized Dietary Recommendations: A Systematic Review
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  • Internet Archive ID: osf-registrations-zweq3-v1

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This systematic review examines the effectiveness of artificial intelligence (AI)-generated dietary recommendations in improving clinical and psychological outcomes among adults. Personalized nutrition is increasingly recognized as essential for managing chronic conditions such as type 2 diabetes, irritable bowel syndrome (IBS), and functional constipation. Traditional dietary interventions often overlook individual metabolic variability and behavioral patterns, leading to inconsistent outcomes. To address this gap, we conducted a systematic review following PRISMA 2020 guidelines and registered the protocol post hoc. We searched six major databases (Cochrane, EBSCO, EMBASE, PubMed, SCOPUS, and Web of Science) for peer-reviewed articles published between November 19, 2015, and June 4, 2024. Eleven studies met our inclusion criteria, comprising five randomized controlled trials, five pre-post studies, and one cross-sectional analysis. The included interventions utilized machine learning, deep learning, and IoT-integrated systems to generate personalized dietary plans based on various individual data inputs—such as blood glucose, gut microbiome composition, and self-reported health metrics. Outcomes demonstrated that AI-generated diets consistently improved glycemic control, metabolic health, gastrointestinal symptoms, and psychological well-being, with some studies reporting up to a 72.7% diabetes remission rate and a 39% reduction in IBS symptom severity. Mild side effects were reported in a subset of studies. This review not only evaluates the clinical efficacy of AI-assisted dietary interventions but also highlights challenges related to algorithm transparency, user adherence, data privacy, and generalizability. Our findings underscore the potential of AI in advancing precision nutrition, while emphasizing the need for further rigorous and inclusive research to inform scalable and equitable implementation in real-world settings.

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"Artificial Intelligence Applications To Personalized Dietary Recommendations: A Systematic Review" is available for download from The Internet Archive in "data" format, the size of the file-s is: 1.06 Mbs, and the file-s went public at Sun Apr 20 2025.

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  • Added Date: 2025-04-20 01:20:48
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