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

Family history, body mass index (BMI), and ethnicity are three key, well-established determinants of susceptibility to type 2 diabetes mellitus (T2DM), reflecting genetic predisposition, modifiable metabolic risk, and biological as well as social influences, respectively. These factors interact in complex, non-linear patterns that are not fully captured by conventional risk prediction models. This review examines how artificial intelligence (AI) and machine learning approaches can integrate these variables to improve risk stratification and early identification of individuals at high risk of T2DM. By leveraging large-scale, longitudinal datasets, data-driven models facilitate the capture of population-level heterogeneity and identify risk patterns that extend beyond static thresholds. Incorporating AI-enhanced prediction tools into clinical and public health settings could enable more timely, targeted, and equitable interventions. Ultimately, integrating advances in AI with a deeper understanding of the interplay between BMI, ethnicity, and genetic predisposition may support more personalised prevention strategies and risk-stratified care pathways for T2DM.