Do machine learning and deep learning models using ECG signals accurately predict diabetes and prediabetes?
While AI-based ECG analysis demonstrates high internal accuracy (>90%) for detecting diabetes, clinical viability is currently limited by a lack of external validation and standardized methods.
Diabetes is a major global health challenge, with many individuals remaining undiagnosed due to the limitations of traditional screening methods. Artificial intelligence (AI)-based electrocardiogram (ECG) analysis offers a promising, non-invasive approach for the early detection of diabetes. This systematic review aims to critically evaluate machine learning (ML) and deep learning (DL) models developed for non-invasive prediction of diabetes and prediabetes using ECG signals. A comprehensive literature search was conducted across PubMed, Embase, Web of Science, IEEE Xplore, and ACM Digital Library in accordance with PRISMA 2020 guidelines. Twenty-five studies met the inclusion criteria. Extracted data included ECG input types, model architectures, preprocessing methods, feature sets, validation strategies, and performance metrics. Most studies used small, single-site, cross-sectional datasets, with sample sizes ranging from 24 to over 190,000 individuals. ECG preprocessing methods varied widely, including filtering, normalization, and decomposition. Features were extracted from time, frequency, morphological, and non-linear domains, though formal feature selection was applied inconsistently. ML and DL models reported high internal accuracy (>90%) but most lacked external validation and subgroup performance assessments. Notably, no study specifically focused on rural or underserved populations, and only one provided open-source code. AI-based ECG analysis demonstrates strong potential for detecting diabetes; however, current research is limited by generalizability issues, lack of standardized methods, poor external validation, and insufficient transparency. Future studies should prioritize rigorous validation, reproducibility, fairness audits, and applications in rural and underserved settings to ensure equitable and clinically viable deployment of these models.
Balakrishnan et al. (2026) studied this question.