Artificial Intelligence Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways

Abstract

Background/Objectives: Obesity is a chronic, relapsing disease with a widening gap between clinical need and the availability of specialist care. Artificial intelligence (AI) may enable earlier risk detection, more precise phenotyping, and scalable behavioural support across obesity treatment pathways. This narrative review synthesises con-temporary AI applications across the obesity care continuum and evaluates their translational readiness. Methods: A targeted search of PubMed/MEDLINE and Google Scholar (January 2024–January 2026) was conducted, complemented by citation chaining. Evidence was syn-thesised across four domains: (1) risk prediction and screening, (2) environmental and behavioural determinants, (3) multimodal phenotyping and precision stratification, and (4) AI-enabled lifestyle interventions and behavioural coaching (AIBC). Results: EHR-based models demonstrate clinically useful discrimination for early risk identification. Multimodal approaches refine stratification beyond BMI-centric classi-fication. AIBC platforms show emerging evidence of clinically meaningful weight loss, including non-inferiority to human coaching, but long-term effectiveness, generalisa-bility, and equity remain insufficiently established. Conclusions: AI is positioned to become a core enabler of personalised obesity path-ways. Safe translation requires external validation, bias auditing, transparent report-ing, human oversight, and post-deployment surveillance aligned with clinical guide-lines and regulatory expectations.

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