Song WH, Wu XL, Tse G, Liu T. Advances in the application of artificial intelligence-enhanced electrocardiogram to non-cardiovascular diseases. World J Cardiol 2026; 18(7): 119678 [DOI: 10.4330/wjc.119678]
Corresponding Author of This Article
Tong Liu, PhD, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi District, Tianjin 300211, China. liutongdoc@126.com
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Cardiac & Cardiovascular Systems
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review-article
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Song WH, Wu XL, Tse G, Liu T. Advances in the application of artificial intelligence-enhanced electrocardiogram to non-cardiovascular diseases. World J Cardiol 2026; 18(7): 119678 [DOI: 10.4330/wjc.119678]
World J Cardiol. Jul 26, 2026; 18(7): 119678 Published online Jul 26, 2026. doi: 10.4330/wjc.119678
Advances in the application of artificial intelligence-enhanced electrocardiogram to non-cardiovascular diseases
Wen-Hua Song, Xing-Liang Wu, Gary Tse, Tong Liu
Wen-Hua Song, Xing-Liang Wu, Gary Tse, Tong Liu, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
Gary Tse, School of Nursing and Health Sciences, Hong Kong Metropolitan University, Hong Kong 000000, China
Co-first authors: Wen-Hua Song and Xing-Liang Wu.
Author contributions: Song WH and Wu XL conceived the study and wrote the paper as co-first authors; Tse G edited and revised the manuscript; Liu T reviewed and revised the manuscript. All authors have read and approved the final manuscript.
AI contribution statement: We confirm that no AI tools were used in the preparation of this manuscript. Specifically, no AI tools such as ChatGPT, Grammarly, or DeepL were used; no portion of the main text (Abstract, Introduction, Discussion, and Conclusion) was AI-generated; no AI tool was used for language polishing, translation or writing assistance; no AI tool participated in the design of this opinion review; and no images in the manuscript were generated by AI.
Supported by Tianjin Key Medical Discipline Construction Project, No. TJYXZDXK-3-006B.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Tong Liu, PhD, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University, No. 23 Pingjiang Road, Hexi District, Tianjin 300211, China. liutongdoc@126.com
Received: February 5, 2026 Revised: March 17, 2026 Accepted: June 10, 2026 Published online: July 26, 2026 Processing time: 164 Days and 23.6 Hours
Abstract
The proliferation of electronic health records has prompted the development of healthcare big data and application of artificial intelligence (AI) for clinical diagnosis and treatment. AI-electrocardiogram (AI-ECG), valued for its non-invasive nature, operational simplicity, and broad accessibility, has demonstrated considerable utility in disease identification and management. Importantly, the scope of AI-ECG is not confined to cardiovascular diseases. A growing body of evidence highlights its additional value for non-cardiovascular conditions such as diabetes, liver diseases, kidney diseases, infectious diseases and electrolyte imbalances. This opinion review summarizes the research progress of AI-ECG in non-cardiovascular diseases and discusses its clinical implications to guide attention towards these novel applications.
Core Tip: This article systematically reviews the latest advances in artificial intelligence-enhanced electrocardiogram for the diagnosis and evaluation of non-cardiovascular diseases. It summarizes its value in identifying and predicting risks across various conditions, including liver and kidney diseases, pulmonary embolism, and electrolyte disturbances. The opinion review also discusses the strengths, limitations, and future directions of this technology, aiming to inform clinicians and support clinical decision-making.
