Applications of artificial intelligence in cardiovascular risk detection and prediction among adults with obesity: a scoping review
By
Mario Andrés Torres Torres
Mariana González Garcés
Jerónimo Cárdenas Montoya
Valeria Concha Fernández
Erwin Hernando Hernández Rincón
May 12, 2026
Clinical Scorecard: Utilization of Artificial Intelligence for Identifying and Forecasting Cardiovascular Risk in Obese Adults: A Scoping Review
At a Glance
Category Detail
Condition Obesity and cardiovascular risk Key Mechanisms Insulin resistance, sympathetic nervous system activation, endothelial dysfunction, prothrombotic state, chronic inflammation Target Population Adults with obesity Care Setting Clinical practice
Key Highlights
Obesity significantly increases cardiovascular risk, necessitating accurate risk assessment. Conventional risk prediction tools often fail in obese populations due to metabolic heterogeneity. AI shows potential for improved cardiovascular risk detection and prediction. Tree-based ensemble methods are frequently used in AI models for risk assessment. Limited external validation and methodological heterogeneity hinder clinical implementation. Guideline-Based Recommendations
Diagnosis
Systematic cardiovascular risk stratification is recommended for personalized preventive interventions. Management
AI-based models should be considered as complementary tools for cardiovascular risk assessment. Monitoring & Follow-up
Future research should focus on robust external validation and standardized outcomes. Risks
Conventional models may underestimate cardiovascular risk in obese individuals. Patient & Prescribing Data
Adults with obesity
AI can enhance risk stratification and disease detection in this population.
Clinical Best Practices
Prioritize prospective study designs in future research. Ensure robust external validation of AI models. Standardize outcome definitions for cardiovascular risk assessment. Related Resources & Content
