Background: Electrolyte imbalances significantly affect heart function, making electrocardiography (ECG) a crucial non-invasive tool. This study systematically reviewed and meta-analyzed AI models' diagnostic accuracy for detecting these imbalances from ECG, aiming to enhance early detection and improve cardiac care. Methods: We searched 9 databases and reference lists. Two reviewers assessed bias via the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2). Test performance data were extracted into 2x2 tables and pooled estimates of specificity, sensitivity, and diagnostic odds ratio (DOR) were calculated using a bivariate random-effects model, presented in forest plots and summary receiver operating characteristic curves. We explored heterogeneity by meta-regression, examining internal/external datasets and the number of leads. Results: 21 studies addressing potassium, calcium, and sodium were included. A meta-analysis was conducted only on potassium imbalances (10 studies), covering over 600,000 ECGs from five countries, mostly 12-lead. Among eight studies focused on hyperkalemia, pooled sensitivity, specificity, and DOR were 0.856 (95% CI: 0.829-0.879), 0.788 (0.744-0.826), and 21.8 (17.8-26.7). For hypokalemia (six studies), pooled sensitivity, specificity, and DOR were 0.824 (0.785-0.856), 0.724 (0.668-0.774), and 12.27 (9.15-16.47). QUADAS-2 assessment showed a 52% high risk of bias in patient selection, mainly due to inadequate sampling details and case-control approaches. Conclusion: AI models can detect ECG-based electrolyte abnormalities, particularly hyperkalemia, and valuable in ICU settings requiring frequent electrolyte assessments and in home monitoring for patients with end-stage renal disease. However, larger retrospective and prospective studies across diverse clinical settings, hospitals, ethnic groups, countries, and regions are warranted.

The authors have declared no competing interest.

This study did not receive any funding

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