Potential Use and Limitation of Artificial Intelligence to Screen Diabetes Mellitus in Clinical Practice: A Literature Review.

Abstract

The burden of undiagnosed diabetes mellitus (DM) is substantial, with approximately 240 million individuals globally unaware of their condition, disproportionately affecting low- and middle-income countries (LMICs), including Indonesia. Without screening, DM and its complications will impose significant pressure on healthcare systems. Current clinical practices for screening and diagnosing DM primarily involve blood or laboratory-based testing which possess limitations on access and cost. To address these challenges, researchers have developed risk-scoring tools to identify high-risk populations. However, considering generalizability, artificial intelligence (AI) technologies offer a promising approach, leveraging diverse data sources for improved accuracy. AI models (i.e., machine learning and deep learning) have yielded prediction performances of up to 98% in various diseases. This article underscores the potential of AI-driven approaches in reducing the burden of DM through accurate prediction of undiagnosed diabetes while highlighting the need for continued innovation and collaboration in healthcare delivery.