Artificial Intelligence for Diabetes Complication Prediction: A Systematic Review of Current Applications and Future Directions

Importance and aims: Diabetes can lead to microvascular and macrovascular complications. Modeling the complex relationships between risk factors has motivated the use of Artificial Intelligence (AI) to develop predictive models. Recent advancements, including foundation models and generative AI, have significantly changed how this technology is applied across various contexts. In this review, we summarize the current state of research on AI for predictive diabetes complications, investigating the present and future implications of these innovations. Methods: We conducted the literature search on PubMed, Scopus, Ovid MEDLINE, CINAHL, and IEEE databases. Our analysis focused on predicted complications, population characteristics, use of AI-based approaches, models’ performance, predictor variables, and feature importance evaluation results. Results: The 49 studies selected in our analysis considered different conditions as prediction outcomes. Eye-related complications were included in 29 studies (59%), emerging as the most frequent predicted diseases. Among the 48 studies employing AI algorithms specifically for the prediction task, 26 (54%) developed only Machine Learning models, 4 (8%) only Deep Learning models, and 18 (38%) applied both approaches. Foundation models and recent AI innovations included in the query were not used by any study. Moreover, only five studies (10%) dealt with unstructured data (signals and images). In the feature importance evaluation, age and glycated hemoglobin consistently emerged as important predictors. Conclusions: Despite the extensive existing literature on AI for predicting diabetes complications, several emerging challenges persist. These include the effective utilization of unstructured data and the integration of recent advancements introduced by foundation models and generative AI.