Transforming Obesity Care Through Artificial Intelligence: Real-Case Implementations and Personalized Solutions
Mayyas Al‐Remawi, Rami A. Abdel‐Rahem, Faisal Al‐Akayleh, Faisal Aburub, Ahmed S.A. Ali Agha
Abstract
Obesity is a multifactorial condition that significantly increases the risk of metabolic syndrome, cardiovascular disease, and various other comorbidities. Conventional obesity management strategies-encompassing body mass index (BMI) measurements, dietary counseling, and caloric tracking-often fail to account for the heterogeneity of individual genetic, behavioral, and metabolic factors. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a transformative approach, offering data-driven, personalized solutions that integrate electronic health records (EHRs), wearable sensor data, and multi-omics information. This article examines contemporary AI applications for obesity care, highlighting real-case implementations, technical and ethical considerations, and the future potential for optimizing patient outcomes. By leveraging advanced analytics, healthcare providers can enhance diagnostic precision, tailor interventions to individualized needs, and support sustained weight control, thus redefining the paradigm of obesity management.