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

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 contemporary 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 synthesised 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: Electronic health record (EHR)-based models demonstrate clinically useful discrimination for early risk identification. Multimodal approaches refine stratification beyond body mass index (BMI)-centric classification. AI-enabled behavioural coaching (AIBC) platforms show emerging evidence of clinically meaningful weight loss, including non-inferiority to human coaching; however, long-term effectiveness, generalisability, and equity remain insufficiently established. Conclusions: AI is positioned to become a core enabler of personalised obesity pathways. Safe translation requires external validation, bias auditing, transparent reporting, human oversight, and post-deployment surveillance aligned with clinical guidelines and regulatory expectations.