Recent research spanning from 1990 to 2023 has unveiled significant advancements in machine learning (ML) and artificial intelligence (AI) for predicting diabetic complications such as retinopathy (DR), kidney disease (DKD), and neuropathy (DN). Across 74 studies analyzed, a notable increase in global ML research emerged, particularly in DKD prediction. These models exhibited impressive accuracy, with DKD models outperforming others. However, there’s a call for more attention to predicting DR and DN. Validating these models externally and adhering to guidelines are crucial steps for their widespread adoption.

This comprehensive review sheds light on the remarkable progress in leveraging ML and AI to predict diabetic complications, offering promising insights into improving patient outcomes and quality of care. The findings underscore the transformative potential of these technologies in revolutionizing diabetic care, with DKD research leading the way in terms of both publication volume and prediction performance. However, the need for more extensive research on predicting DR and DN is apparent, highlighting areas for future exploration and innovation.

For those interested in delving deeper into this groundbreaking research, the full article provides valuable insights and analysis. By exploring the methodologies, results, and implications outlined in the review, stakeholders can gain a deeper understanding of the current landscape of ML/AI in diabetic complication prediction and its implications for clinical practice. You can access the full article here: