Methodological Approaches to and Reported Performance of Applications of Automated Machine Learning in Diabetes Risk Prediction: Rapid Review.

Background: Type 2 diabetes (T2D) is a complex, chronic condition that imposes a substantial burden on health care systems. Prevention and early detection are critical to mitigating its impact. Automated machine learning (AutoML) models have the potential to predict individual risk and guide personalized interventions. However, their clinical deployment remains limited due to the retrospective nature of most datasets, a lack of external validation, and heterogeneity in variable selection. Objective: This study aimed to map AutoML approaches applied to T2D risk prediction, with a specific focus on their ability to integrate clinical, behavioral, environmental, and genomic data. Methods: A PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses)-guided rapid review was conducted across 6 databases (PubMed, Scopus, Web of Science, IEEE Xplore, Google Scholar, and Embase) to identify empirical studies (published between 2015 and 2025) that used AutoML tools for T2D prediction based on at least 2 data types (eg, clinical, behavioral, environmental, and genomic). Screening, data extraction, and synthesis were performed systematically by 2 independent reviewers, with arbitration by ChatGPT acting as an artificial intelligence-based third reviewer. Results: In total, 13 studies met the inclusion criteria. Methodological diversity ranged from conventional machine learning with manual feature selection to partially or fully automated pipelines using tools such as the Tree-Based Pipeline Optimization Tool, H2O AutoML, or Azure Machine Learning. Reported performance varied (area under the curve=0.74-0.99); however, external validation was uncommon. Behavioral and environmental data were only partially integrated, and no study incorporated genomic data despite its recognized potential. Most studies lacked transparency and reproducibility, with no public code or pipeline sharing. Conclusions: AutoML holds significant promise for improving T2D risk prediction through automation and model explainability. However, to support clinical adoption and generalizability, future AutoML pipelines must be developed using prospective, multicenter datasets; integrate diverse, harmonized data types, including genomics; and adhere to open science principles of transparency, reproducibility, and interpretability.