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Artificial Intelligence in Healthcare / Nutrition, Genetics, and Disease · Journal article
Diagnostics · August 11, 2026
A consensus or society position rather than new primary data.
This narrative review evaluates emerging applications of machine learning and deep learning to predict childhood obesity and its early complications (T2DM, MASLD) by integrating multi-omic data with socio-psychological metrics. The authors conclude that while AI shows potential for precision medicine in this population, current evidence is limited by heterogeneous datasets and lack of external validation, necessitating large-scale prospective cohorts before clinical implementation.
Narrative review. Eligible studies included original research articles, systematic reviews, and clinical guidelines addressing AI applications in childhood obesity and comorbidities.. Intervention: Artificial intelligence approaches (machine learning, deep learning, multi-omic data integration) for prediction and management of childhood obesity and associated comorbidities..
Machine learning and deep learning have identified specific metabolites, gut flora alterations, neurological pathways, and metabolic SNPs linked to obesity susceptibility ML-driven prognostic models enable risk assessment for MASLD or diabetes progression Integration of multi-omic data (genome, epigenome, transcriptome, metabolome, microbiota) with socio-psychological metrics can predict obesity risk and early complications
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
Clinicians and researchers should recognize AI as an emerging tool for early diagnosis and personalized obesity management, but should recognize that current models lack adequate external validation and large-scale prospective data necessary to justify routine clinical implementation. Implementation will require addressing data privacy, digital literacy, and equitable access barriers.
A narrative review synthesizing the current role of AI in pediatric obesity prediction and management, identifying opportunities and limitations but without primary data or meta-analysis.
As stated by the source record.
Clinicians and researchers should recognize AI as an emerging tool for early diagnosis and personalized obesity management, but should recognize that current models lack adequate external validation and large-scale prospective data necessary to justify routine clinical implementation. Implementation will require addressing data privacy, digital literacy, and equitable access barriers.
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.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
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.
Taken from the source record, never inferred. Follow any of these and new work involving them reaches your briefing.