Article

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

2026-03-11

Abstract excerpt

<h4>Background: </h4> /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...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: fd52e363-4854-57ad-8a90-8fc86587dd9a
DOI: 10.20944/preprints202603.0855.v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Artificial Intelligence Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment PathwaysDOI 10.20944/preprints202603.0855.v1Read

Integrating artificial intelligence and machine learning into risk prediction for type 2 diabetes: A model for early identification of high-risk populations.Semantic proximity Read

Applying Artificial Intelligence to Childhood Obesity: DMT2 and MASLD Risk Predictive ModelsSemantic proximity Read

Discordant Obesity Severity Classification Between the Edmonton Obesity Staging System and the Lancet Commission ModelSemantic proximity Read

Review of the Use of Digital Therapeutics for Obesity Treatment: Toward a Psychological Phenotyping.Semantic proximity Read

Explainable AI to predict a complex multifactorial outcome, childhood obesity: Application to clinical epidemiologySemantic proximity Read

Characterize Disease Progression Subphenotypes in Real World Populations with Overweight and Obesity using a Graph-based Neural Network FrameworkSemantic proximity Read

NutrIA: Development and Internal Validation of a Hybrid Clinical Decision Support System for Personalized Preventive NutritionSemantic proximity Read

Deep phenotyping obesity using EHR data: Promise, Challenges, and Future DirectionsSemantic proximity Read

Development and validation of decision rules models to stratify coronary artery disease, diabetes, and hypertension risk in preventive care: cohort study of returning UK Biobank participantsSemantic proximity Read

Obesity prevalence: comparison of traditional and new classification approaches in a Swiss population-based study(2005-2024)Semantic proximity Read

ADVANCED ARTIFICIAL INTELLIGENCE ENABLED METHODS FOR EARLY DETECTION OF NON-ALCOHOLIC FATTY LIVER DISEASE AND ASSOCIATED HEALTH RISKSSemantic proximity Read

Predicting screening factors for an obesity team’s decision: a large retrospective cohort studySemantic proximity Read