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Artificial Intelligence in Healthcare and Education / Diabetes Treatment and Management · Review
Frontiers in Endocrinology · September 3, 2026
A consensus or society position rather than new primary data.
This structured narrative review found that direct patient-level evidence for explicitly AI-enabled obesity interventions remains limited and heterogeneous across clinical pathways. The strongest weight or metabolic outcome evidence comes from multicomponent digital care programmes, but these do not isolate an AI-specific therapeutic effect. The authors conclude that AI should augment rather than replace clinician-led multidisciplinary care, and that translation to clinical practice requires independent external validation, prospective workflow evaluation, and demonstration of safety, fairness, and cost-effectiveness.
Structured narrative review with SANRA framework. Published literature on artificial intelligence in obesity management across lifestyle, pharmacotherapy, metabolic and bariatric surgery pathways.. Intervention: Artificial intelligence applications across obesity management: drug discovery, natural language processing, phenotype-based treatment, machine learning for risk stratification, risk prediction, operative workflow analysis, readmission str….
Direct patient-level evidence for explicitly AI-enabled obesity interventions remains limited and heterogeneous across lifestyle, pharmacotherapy, and surgical pathways. Strongest weight or metabolic outcome evidence comes from multicomponent digital, automated or hybrid-care programmes, but no AI component was independently evaluated or AI-specific therapeutic effect demonstrated in obesity. AI-assisted drug discovery remains preclinical; natural language processing of glucagon-like peptide-1 receptor agonist narratives can support signal detection but not causal inference or quantitative safety estimation.
No specific safety, fairness, data governance, or cost-effectiveness data reported; review identifies these as future implementation requirements. Direct patient-level evidence for explicitly AI-enabled obesity interventions remains limited and heterogeneous across lifestyle, pharmacotherapy, and surgical pathways.
Clinicians should view AI tools in obesity management as augmentative rather than determinative; clinical readiness varies substantially across pathways, and independent external validation and prospective outcome evaluation are needed before implementation. Current evidence does not support AI as a standalone therapeutic agent or for unsupervised treatment selection.
A structured narrative review synthesizing evidence on AI across obesity management pathways, finding limited direct patient-level evidence for AI-specific effects and recommending AI as a clinical augmentation tool pending further validation.
As stated by the source record.
Quoted from the source exactly as published.
Clinicians should view AI tools in obesity management as augmentative rather than determinative; clinical readiness varies substantially across pathways, and independent external validation and prospective outcome evaluation are needed before implementation. Current evidence does not support AI as a standalone therapeutic agent or for unsupervised treatment selection.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
Obesity is a chronic, relapsing disease that requires long-term lifestyle treatment, pharmacotherapy, and metabolic and bariatric surgery. Artificial intelligence (AI) is increasingly being studied across these pathways, but its clinical readiness varies substantially. This Scale for the Assessment of Narrative Review Articles (SANRA)-informed structured narrative review searched PubMed/MEDLINE, Embase, Scopus, ScienceDirect, and Google Scholar through 1 June 2026, primarily for literature published since 2015, with citation chaining used to identify earlier landmark studies. Evidence was appraised according to study design, validation, clinical utility, workflow integration, and demonstrated patient-level benefit. Direct patient-level evidence for explicitly AI-enabled obesity interventions remains limited and heterogeneous. The strongest weight or metabolic outcome evidence largely comes from multicomponent digital, automated or hybrid-care programmes, including studies in which no AI component was independently evaluated or in which diabetes and HbA1c constituted the primary clinical context and endpoint. These findings support digital-care delivery but should not be interpreted as evidence of an AI-specific therapeutic effect in obesity. AI-assisted drug discovery remains preclinical, while natural language processing of glucagon-like peptide-1 receptor agonist narratives can support signal detection but not causal inference or quantitative safety estimation. Phenotype-based treatment and a machine-learning-assisted genetic risk score suggest potential for responder stratification, but within the eligible evidence included in this review, no externally validated, prospectively implemented AI prescribing system was found to have demonstrated improved patient outcomes. In metabolic and bariatric surgery, AI may support risk prediction, operative workflow analysis, readmission stratification, weight-trajectory modelling, and digital follow-up; most studies, however, remain retrospective or internally validated and rarely assess calibration, actionable thresholds, or prospective impact. Large language models may assist education and drafting, but current evidence does not support unsupervised treatment or procedure selection. AI should therefore augment, not replace, clinician-led multidisciplinary obesity care. Translation will require independent external validation, prospective workflow evaluation, patient-centred outcomes, safety, fairness, data governance, and cost-effectiveness.
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