Artificial Intelligence-Driven Weight Management: Current Evidence and Clinical Implications.
Obesity remains one of the most urgent global public health challenges, necessitating innovative and scalable strategies for effective weight management. This narrative review aims to synthesize current evidence (2021-2026) on the role of artificial intelligence (AI) in weight loss and obesity manag...
| Publicado en: | Lokman Hekim Health Sciences Vol. 6; no. 2; pp. 360 - 372 |
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| Autor principal: | |
| Formato: | review tables/charts Journal Article |
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KARE Publishing
Jun2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194639962&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194639962 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 27917835 N1MV jtl: Lokman Hekim Health Sciences issn: 27917835 maglogo: N pubinfo: dt: Jun2026 vid: 6 iid: 2 pid: 62027 pub: KARE Publishing artinfo: ui: 194639962 194639962 194639962 10.14744/lhhs.2026.66601 194639962 ppf: 360 ppct: 12 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence-Driven Weight Management: Current Evidence and Clinical Implications. aug: au: Servi, Nursena Nahya affil: Department of Nutrition and Dietetics, Faculty of Health Sciences, Lokman Hekim University, Ankara, Türkiye sug: subj: Artificial Intelligence Utilization Weight Control Obesity Therapy Medical Practice, Evidence-Based Dietetics Digital Health Machine Learning Algorithms Mentorship Nutritionists Public Health Biological Markers Blood Health Care Delivery Decision Support Systems, Clinical Mobile Applications Internet of Things Treatment Outcomes ab: Obesity remains one of the most urgent global public health challenges, necessitating innovative and scalable strategies for effective weight management. This narrative review aims to synthesize current evidence (2021-2026) on the role of artificial intelligence (AI) in weight loss and obesity management, and to evaluate its clinical potential, limitations, and future directions. Recent advances in AI, including machine learning and deep learning techniques, have introduced novel opportunities for personalized nutrition, predictive modeling, and digitally supported behavioral interventions. The literature indicates that AI-driven systems show substantial potential in predictive weight loss modeling, reinforcement learning-based treatment optimization, digital coaching platforms, and biomarker-integrated personalization strategies. Importantly, while AI technologies may enhance scalability and personalization, they should be positioned as clinical decision-support tools rather than replacements for dietitians and healthcare professionals. However, the field remains heterogeneous, with a limited number of long-term randomized controlled trials, variable methodological transparency, and insufficient external validation of predictive models. While AI technologies may enhance scalability and personalization, they should be positioned as clinical decision-support tools rather than replacements for dietitians and healthcare professionals. Ethical considerations, data governance, and algorithmic transparency remain critical for safe and responsible implementation. Overall, AI represents a promising adjunct in weight management; however, its integration into clinical nutrition practice requires rigorous validation and interdisciplinary collaboration. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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