Artificial Intelligence-Assisted Nutritional Counseling for Weight Management in Adult Obesity: A Systematic Review of Randomized Controlled Trials and Primary Studies
Aug 2026· International journal of medical science and health research· 0 citations
TL;DR
AI-assisted nutritional counseling demonstrates robust, clinically meaningful efficacy across multiple outcome domains, and Precision nutrition algorithms integrating postprandial glycemic response prediction and gut microbiome profiling achieved the most comprehensive cardiometabolic optimization.
Abstract
Introduction: The global obesity pandemic affects over 2.11 billion adults, with projections exceeding 3.80 billion by 2050. Conventional nutritional counseling modalities face critical limitations in scalability, individualization, and long-term adherence. Artificial intelligence (AI)-assisted nutritional counseling, employing machine learning, deep learning, natural language processing, and reinforcement learning algorithms, represents a transformative paradigm enabling personalized, real-time dietary interventions at population scale.
Methods: This systematic review was conducted following PRISMA 2020 guidelines. Eligible studies comprised randomized controlled trials and primary prospective studies evaluating AI-assisted nutritional interventions in adults (≥18 years) with overweight (BMI ≥25 kg/m²) or obesity (BMI ≥30 kg/m²). Fifteen pre-specified outcome domains were evaluated. Risk of bias was assessed using Cochrane RoB 2.0 and ROBINS-I tools.
Results: Eighteen studies (n=14,732 participants) met eligibility criteria. AI-assisted interventions demonstrated statistically significant reductions in body weight (MD −1.60 kg to −12.3%; 15/18 studies, p<0.01), BMI (MD −0.59 to −1.26 kg/m²; 12/14 studies, p<0.05), HbA1c (MD −0.28% to −2.9%; 9/11 studies), and fasting plasma glucose (7/8 studies). Significant improvements were observed across cardiometabolic, anthropometric, dietary quality, physical activity, health-related quality of life, and patient engagement domains. AI-led interventions demonstrated non-inferiority to human coaching and superiority to standard care.
Discussion: AI-assisted nutritional counseling demonstrates robust, clinically meaningful efficacy across multiple outcome domains. Precision nutrition algorithms integrating postprandial glycemic response prediction and gut microbiome profiling achieved the most comprehensive cardiometabolic optimization. AI-integrated mHealth applications demonstrated superior population-level scalability. Methodological limitations include heterogeneity across AI modalities, short intervention durations, and underrepresentation of low- and middle-income country populations.
Conclusion: AI-assisted nutritional counseling is efficacious, safe, scalable, and non-inferior to human coaching for adult obesity management across ≥10 clinically relevant outcome domains. Integration into multidisciplinary obesity management clinical pathways is recommended, supported by long-term, equity-focused randomized controlled trials with standardized outcome reporting frameworks.
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.
Unknown authors· Frontiers in Endocrinology· 0 citations
Overall, AI provides evidence-informed decision support for dietitians and social work professionals, helping to develop a holistic care ecosystem that optimizes the well-being of older adults.
İrem Nur Şahin Anılgan, Onur Zeki Anılgan· İstanbul Gelişim Üniversites...· 1 citation
Background: Type 2 diabetes mellitus (T2DM) has become a global epidemic in recent decades, contributing substantially to morbidity, mortality, and economic burden. The increasing prevalence of T2DM is largely attributable to ageing populations, urbanization, unhealthy diets, inadequate physical activity, and rising obesity rates. While lifestyle modification is a primary strategy for T2DM prevention and management, long-term behavioural modification is challenged by poor adherence, limited access to healthcare, and suboptimal counselling in personalised interventions. Objective: This systematic review with narrative synthesis aimed to evaluate the impact of artificial intelligence-based lifestyle interventions on glycaemic, anthropometric, lifestyle, and self-management outcomes among individuals with T2DM, prediabetes, or at high risk of T2DM. Methods: This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Literature was searched in PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane CENTRAL, IEEE Xplore, CINAHL, ProQuest, ClinicalTrials.gov, and the WHO International Clinical Trials Registry Platform (ICTRP) for studies published from 1 January 2020 to 31 December 2025. Eligible studies included adults with prediabetes, at high risk of developing T2DM, or with T2DM who received an AI-enabled lifestyle intervention. Interventions included AI-enabled mobile applications, machine learning algorithms, conversational chatbots, virtual health coaches, digital therapeutics, predictive analytics, and wearable-based AI platforms. Randomised controlled trials or controlled clinical studies reporting glycaemic, anthropometric, behavioural, or self-management outcomes were eligible. Results: The literature search yielded 1,186 studies, of which seven RCTs involving 2,369 participants met the inclusion criteria and were included in the narrative synthesis. The included studies originated from the United States, Australia, Spain, Czech Republic, Sweden, and Singapore. AI technologies included mHealth applications, conversational AI, digital behavioural therapies, AI coaching systems, and AI-assisted wearable technology, with intervention periods ranging from 3 to 12 months. Overall, AI-supported lifestyle interventions showed positive effects on glycaemic control, with all but one study reporting significant reductions in HbA1c. Positive effects were also reported for body weight, body mass index, physical activity, dietary adherence, medication adherence, patient engagement, and diabetes self-care activities. Conclusion: AI-supported lifestyle interventions show considerable promise as an adjunct to standard diabetes management through individualised recommendations, real-time monitoring, and ongoing behavioural guidance. These systems may help optimise diabetes prevention and management, particularly in resource-constrained healthcare settings. Further well-designed multicentre RCTs with standardised AI methods, longer follow-up periods, and economic evaluations are warranted.
Pooja Mary Vaishali V, Balabaskaran S, Solomon Rajkumar P et al.· Adolescência e Saúde· 0 citations
Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise.
K. Mauldin, Anthony D. Pham, Sneha Dodaballapur et al.· Nutrients· 0 citations
Pediatric obesity is a complex, multifactorial pandemic with serious early-onset comorbidities, including prediabetes, type 2 diabetes, metabolic dysfunction-associated steatotic liver disease (MASLD), and cardiovascular disorders. While lifestyle modifications and the Mediterranean diet remain primary interventions, artificial intelligence (AI) is emerging as a critical tool for early diagnosis and personalized management. This review evaluates the current role of AI in predicting and treating childhood obesity and its complications. A literature search was conducted on PubMed and Google Scholar for English-language articles published from 2015 onward. Search terms included combinations of keywords related to “obesity”, “pediatric”, “comorbidities” (e.g., MASLD, diabetes), and “artificial intelligence” (e.g., machine learning, deep learning, multi-omics). Eligible study types ranged from original articles to systematic reviews and clinical guidelines. By integrating multi-omic data (genome, epigenome, transcriptome, metabolome, microbiota) with socio-psychological metrics, AI can predict obesity risk and early complications. Machine learning (ML) and deep learning have successfully identified specific metabolites, gut flora alterations, neurological pathways, and metabolic SNPs linked to obesity susceptibility. Furthermore, ML-driven prognostic models enable risk assessment for MASLD or diabetes progression, while specialized software supports remote lifestyle monitoring and tailored dietary interventions. AI has the potential to revolutionize pediatric obesity management through precision medicine. However, challenges regarding data privacy, digital literacy, and equitable access persist. Because current evidence relies heavily on limited and heterogeneous pediatric datasets, large-scale, well-characterized, and externally validated cohorts are essential to establish the clinical applicability of AI models before routine implementation.
Marianna Amitrano, Gianluca Mondillo, Mario Emiliano et al.· Diagnostics· 0 citations
Artificial intelligence (AI) is entering child nutrition through dietary assessment, malnutrition forecasting, clinical decision support, meal recommendation, conversational interventions, and food-environment monitoring. The consequences of these applications converge in everyday eating. This Mini Review synthesizes evidence on how AI measures nutritional states and mediates food choice, communication, sensory acceptance, family practice, and digital exposure. The evidence supports two connected functions. As nutritional intelligence, AI converts clinical, dietary, and environmental data into assessments or predictions. As gastronomic mediation, it participates in decisions about what foods are noticed, recommended, prepared, discussed, and accepted. Direct pediatric validation is strongest for bounded assessment and forecasting tasks. Child meal-planning studies and generative-AI evaluations based on standardized adolescent profiles reveal a gap between nutrient optimization, culinary coherence, and nutritional safety. A large adolescent chatbot trial combined scalable delivery with null intention-to-treat effects on diet quality and BMI trajectory. Co-design and behavioral studies further identify familiarity, texture, participation, and caregiver involvement as central design variables. We propose five iterative translational gates: technical validity, nutritional validity, behavioral acceptability, contextual legitimacy, and real-world effectiveness and implementation. Future research should combine age-specific nutritional constraints with sensory and cultural knowledge, evaluate performance across food cultures, preserve professional and caregiver oversight, and test outcomes in homes, schools, clinics, and digital food environments. AI can advance child-focused gastronomy by translating computational outputs into safe, culturally meaningful, and developmentally appropriate eating practices.
L. Xing, Xue Wu· Frontiers in Nutrition· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
