ARTIFICIAL INTELLIGENCE AND PRECISION MEDICINE IN OBESITY MANAGEMENT: CURRENT APPLICATIONS, CHALLENGES, AND FUTURE DIRECTIONS

Abstract

Obesity is a complex, chronic, and heterogeneous disease that affects more than one billion people worldwide and represents one of the greatest public health challenges of the twenty-first century. Despite significant advances in pharmacotherapy, including glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual incretin-based therapies, considerable interindividual variability in treatment response remains a major obstacle to effective obesity management. The emergence of precision medicine and artificial intelligence (AI) has created new opportunities to address this challenge by enabling individualized risk assessment, phenotype-based treatment selection, and prediction of therapeutic outcomes. Recent developments in genomics, machine learning, digital health technologies, and large-scale healthcare datasets have facilitated the transition from a one-size-fits-all approach toward personalized obesity care. AI-based models can integrate genetic, metabolic, behavioral, and clinical data to identify obesity subtypes, predict responses to pharmacological interventions, and support clinical decision-making. Furthermore, digital phenotyping and wearable technologies provide continuous monitoring of lifestyle behaviors and physiological parameters, allowing dynamic adaptation of treatment strategies. This review examines the current role of AI and precision medicine in obesity management, focusing on obesity phenotyping, genomic risk stratification, predictive analytics, digital health interventions, and AI-assisted pharmacotherapy selection. Additionally, ethical, regulatory, and implementation challenges are discussed. The integration of artificial intelligence with precision medicine has the potential to transform obesity treatment by improving therapeutic effectiveness, reducing healthcare costs, and advancing individualized patient care.