Artificial Intelligence Algorithms for Early Detection and Risk Stratification of Type 2 Diabetes Mellitus, Multimodal Bioinformatic Phenotyping, and Its Complications: A Technical Review of Classical Machine Learning, Deep Learning, and Post-Hoc Feature Attribution

Type 2 diabetes mellitus (T2DM) affects hundreds of millions of people worldwide, and nearly half of all cases remain undiagnosed. This technical review, conducted under conducted under the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and structured around the Population, Intervention, Comparison, Outcome and Context (PICOC) framework, identifies and synthesises the evidence on artificial intelligence (AI) algorithms for the early detection of T2DM and the risk stratification of its complications, evaluating their performance against conventional diagnostic methods and characterising their clinical implementation contexts. A systematic search in Scopus with a four-stage screening process, synthesised across five research dimensions, yielded 255 records, of which 47 studies met the inclusion criteria (2025–2026). Ensemble methods based on gradient boosting, particularly particularly extreme gradient boosting (XGBoost), emerged as the dominant primary modelling approach, while logistic regression was the most frequently reported algorithm, predominantly as a baseline comparator, with area under the receiver operating characteristic curve (AUC-ROC) values ranging from 0.63 to 0.97. AI models consistently outperformed glycated haemoglobin (HbA1c), whose reported sensitivity for prediabetes was only 49%, reaching accuracies of up to 96.7%, although that upper figure comes from a narrowly defined cohort with a high baseline prevalence and does not represent general screening performance. Multimodal approaches achieved the highest discrimination, combining wearables, continuous glucose monitoring, and genomics (AUC 0.96 internal, 0.90 external, for the separation of self-reported glycaemic states), and a real-world clinical deployment integrated into electronic health records improved glycaemic control outcomes among high-risk patients. An assessment of the eight anchor studies with thePrediction model Risk Of Bias ASsessment Tool updated for AI (PROBAST + AI) found high concern regarding quality in six of them, driven mainly by cross-sectional designs and self-reported outcomes. Overall, AI stands out as a viable and scalable complement to conventional screening, although gaps remain in multicentre prospective validation, reporting standardisation, and algorithmic equity across populations.