{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T22:26:41Z","timestamp":1792016801735,"version":"4.4.0"},"reference-count":45,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>Background\/Objectives: Acute myocardial infarction (MI) is a major cause of death worldwide, and it imposes a heavy burden on health care systems. Although diagnostic methods have improved, detecting the disease early and accurately is still difficult. Recently, AI has demonstrated increasing capability in improving ECG-based MI detection. From this perspective, this scoping review aimed to systematically map and evaluate AI applications for detecting MI through ECG data. Methods: A systematic search was performed in Ovid MEDLINE, Ovid Embase, Web of Science Core Collection, and Cochrane Central. The search covered publications from 2015 to 9 October 2024; non-English articles were included if a reliable translation was available. Studies that used AI to diagnose MI via ECG were eligible, and studies that used other diagnostic modalities were excluded. The review was performed per the PRISMA extension for scoping reviews (PRISMA-ScR) to ensure transparent and methodological reporting. Of a total of 7189 articles, 220 were selected for inclusion. Data extraction included parameters such as first author, year, country, AI model type, algorithm, ECG data type, accuracy, and AUC to ensure all relevant information was captured. Results: Publications began in 2015 with a peak in 2022. Most studies used 12-lead ECGs; the Physikalisch-Technische Bundesanstalt database and other public and single-center datasets were the most common sources. Convolutional neural networks and support vector machines predominated. While many reports described high apparent performance, these estimates frequently came from relatively small, single-source datasets and validation strategies prone to optimism. Cross-validation was reported in 57% of studies, whereas 36% did not specify their split method, and several noted that accuracy declined under inter-patient or external validation, indicating limited generalizability. Accordingly, headline figures (sometimes \u226599% for accuracy, sensitivity, or specificity) should be interpreted in light of dataset size, case mix, and validation design, with risks of spectrum\/selection bias, overfitting, and potential data leakage when patient-level independence is not enforced. Conclusions: AI-based approaches for MI detection using ECGs have grown quickly. Diagnostic performance is limited by dataset and validation issues. Variability in reporting, datasets, and validation strategies have been noted, and standardization is needed. Future work should address clinical integration, explainability, and algorithmic fairness for safe and equitable deployment.<\/jats:p>","DOI":"10.3390\/jcm14196792","type":"journal-article","created":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T13:48:05Z","timestamp":1758808085000},"page":"6792","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence for Myocardial Infarction Detection via Electrocardiogram: A Scoping Review"],"prefix":"10.3390","volume":"14","author":[{"given":"Sosana","family":"Bdir","sequence":"first","affiliation":[{"name":"Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1936-3966","authenticated-orcid":false,"given":"Mennatallah","family":"Jaber","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Osaid","family":"Tanbouz","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fathi","family":"Milhem","sequence":"additional","affiliation":[{"name":"Neuro-Pal Center, Ramallah P627, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iyas","family":"Sarhan","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8968-7034","authenticated-orcid":false,"given":"Mohammad","family":"Bdair","sequence":"additional","affiliation":[{"name":"Department of Medicine, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thaer","family":"Alhroob","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, University of Toledo Medical Center, Toledo, OH 43614, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Walaa","family":"Abu Alya","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Cleveland Clinic Mercy Hospital, Canton, OH 44708, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0702-7834","authenticated-orcid":false,"given":"Mohammad","family":"Qneibi","sequence":"additional","affiliation":[{"name":"Department of Biomedical Sciences and Basic Clinical Skills, Faculty of Medicine and Allied Health Sciences, An-Najah National University, Nablus P400, Palestine"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,25]]},"reference":[{"key":"ref_1","first-page":"95","article-title":"An update on myocardial infarction","volume":"1","author":"Fathima","year":"2021","journal-title":"Curr. Res. Trends Med. Sci. Technol."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Salari, N., Morddarvanjoghi, F., Abdolmaleki, A., Rasoulpoor, S., Khaleghi, A.A., Hezarkhani, L.A., Shohaimi, S., and Mohammadi, M. (2023). The global prevalence of myocardial infarction: A systematic review and meta-analysis. BMC Cardiovasc. Disord., 23.","DOI":"10.1186\/s12872-023-03231-w"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1093\/ije\/dyq165","article-title":"World Health Organization definition of myocardial infarction: 2008\u201309 revision","volume":"40","author":"Mendis","year":"2011","journal-title":"Int. J. Epidemiol."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Dutta, U., Baruah, S., and Baruah, S. (2025). Artificial Intelligence (Al) in Healthcare, IGI Global.","DOI":"10.4018\/979-8-3693-9735-0.ch001"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"e2317831","DOI":"10.1001\/jamanetworkopen.2023.17831","article-title":"Interfacility Transfer of Uninsured vs Insured Patients with ST-Segment Elevation Myocardial Infarction in California","volume":"6","author":"Ward","year":"2023","journal-title":"JAMA Netw. Open"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1007\/s12245-009-0107-x","article-title":"Overcrowding is associated with delays in percutaneous coronary intervention for acute myocardial infarction","volume":"2","author":"Kulstad","year":"2009","journal-title":"Int. J. Emerg. Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e310","DOI":"10.1161\/CIR.0000000000001025","article-title":"Systems of Care for ST-Segment\u2013Elevation Myocardial Infarction: A Policy Statement From the American Heart Association","volume":"144","author":"Jacobs","year":"2021","journal-title":"Circulation"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1093\/qjmed\/hcl025","article-title":"ECG diagnosis of acute ischaemia and infarction: Past, present and future","volume":"99","author":"Herring","year":"2006","journal-title":"QJM Int. J. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"448","DOI":"10.1007\/s42979-024-02827-z","article-title":"Enhancing Myocardial Infarction Diagnosis: Insights from ECG Image Analysis and Machine Learning","volume":"5","author":"Raghukumar","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_10","unstructured":"Boldireva, A. (2023). Identifying Needs in the Field of Electrocardiogram Analysis to Increase the Accuracy of ECG Interpretation. [Master\u2019s Thesis, Norwegian University of Science and Technology]."},{"key":"ref_11","unstructured":"De Luna, A.B. (2008). Basic Electrocardiography: Normal and Abnormal ECG Patterns, John Wiley & Sons."},{"key":"ref_12","first-page":"118","article-title":"AI in healthcare: Revolutionizing diagnosis and therapy","volume":"3","author":"Zeb","year":"2024","journal-title":"Int. J. Multidiscip. Sci. Arts"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sallam, M., Beaini, C., Mijwil, M.M., and Sallam, M. (2025). AI at the Crossroads of Cost and Care: A SWOT Analysis of Microsoft AI Diagnostic Orchestrator (MAI-DxO) in Clinical Diagnosis. J. Clin. Med. Res.","DOI":"10.37155\/3060-8708-02"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Moreno-S\u00e1nchez, P.A., Garc\u00eda-Isla, G., Corino, V.D., Vehkaoja, A., Brukamp, K., Van Gils, M., and Mainardi, L. (2024). ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review. Comput. Biol. Med., 172.","DOI":"10.1016\/j.compbiomed.2024.108235"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Xiong, P., Lee, S.M.-Y., and Chan, G. (2022). Deep learning for detecting and locating myocardial infarction by electrocardiogram: A literature review. Front. Cardiovasc. Med., 9.","DOI":"10.3389\/fcvm.2022.860032"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.ins.2017.06.027","article-title":"Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals","volume":"415\u2013416","author":"Acharya","year":"2017","journal-title":"Inf. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.bspc.2018.05.013","article-title":"Multiple-feature-branch convolutional neural network for myocardial infarction diagnosis using electrocardiogram","volume":"45","author":"Liu","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.hlc.2020.04.011","article-title":"In-hospital prognostic value of electrocardiographic parameters other than ST-segment changes in acute myocardial infarction: Literature review and future perspectives","volume":"29","author":"Lakhani","year":"2020","journal-title":"Heart Lung Circ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1038\/s41569-020-00503-2","article-title":"Artificial intelligence-enhanced electrocardiography in cardiovascular disease management","volume":"18","author":"Siontis","year":"2021","journal-title":"Nat. Rev. Cardiol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1161\/CIRCULATIONAHA.119.041980","article-title":"Machine learning to predict the likelihood of acute myocardial infarction","volume":"140","author":"Than","year":"2019","journal-title":"Circulation"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W.-C., Lin, C., Lin, C.-S., Tsai, M.-C., Chen, S.-J., Tsai, S.-H., Lin, W.-S., Lee, C.-C., Tsao, T.-P., and Cheng, C.-C. (2021). An artificial intelligence-based alarm strategy facilitates management of acute myocardial infarction. J. Pers. Med., 11.","DOI":"10.3390\/jpm11111149"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kuno, T., Mikami, T., Sahashi, Y., Numasawa, Y., Suzuki, M., Noma, S., Fukuda, K., and Kohsaka, S. (2022). Machine learning prediction model of acute kidney injury after percutaneous coronary intervention. Sci. Rep., 12.","DOI":"10.1038\/s41598-021-04372-8"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"215","DOI":"10.1016\/j.hfc.2014.12.012","article-title":"Hemodynamic support with percutaneous devices in patients with heart failure","volume":"11","author":"Kapur","year":"2015","journal-title":"Heart Fail. Clin."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"652","DOI":"10.3390\/ai4030034","article-title":"Explainable artificial intelligence (XAI): Concepts and challenges in healthcare","volume":"4","author":"Hulsen","year":"2023","journal-title":"AI"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1007\/s41664-018-0068-2","article-title":"On Splitting Training and Validation Set: A Comparative Study of Cross-Validation, Bootstrap and Systematic Sampling for Estimating the Generalization Performance of Supervised Learning","volume":"2","author":"Xu","year":"2018","journal-title":"J. Anal. Test."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1253\/circrep.CR-19-0096","article-title":"Application of Neural Networks to 12-Lead Electrocardiography\u2014Current Status and Future Directions","volume":"1","author":"Goto","year":"2019","journal-title":"Circ. Rep."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1038\/s41597-020-0386-x","article-title":"A 12-lead electrocardiogram database for arrhythmia research covering more than 10,000 patients","volume":"7","author":"Zheng","year":"2020","journal-title":"Sci. Data"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Micali, G., Corallo, F., Pagano, M., Giamb\u00f2, F.M., Duca, A., D\u2019Aleo, P., Anselmo, A., Bramanti, A., Garofano, M., and Mazzon, E. (2024). Artificial Intelligence and Heart-Brain Connections: A Narrative Review on Algorithms Utilization in Clinical Practice. Healthcare, 12.","DOI":"10.3390\/healthcare12141380"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, Y., Ye, J., Li, Y., Luo, Z., Luo, J., and Wan, X. (2025). A Multi-Domain Feature Fusion CNN for Myocardial Infarction Detection and Localization. Biosensors, 15.","DOI":"10.3390\/bios15060392"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1038\/nbt1206-1565","article-title":"What is a support vector machine?","volume":"24","author":"Noble","year":"2006","journal-title":"Nat. Biotechnol."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Kumar, M., Pachori, R.B., and Acharya, U.R. (2017). Automated Diagnosis of Myocardial Infarction ECG Signals Using Sample Entropy in Flexible Analytic Wavelet Transform Framework. Entropy, 19.","DOI":"10.3390\/e19090488"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.asoc.2017.12.001","article-title":"Detection of myocardial infarction in 12 lead ECG using support vector machine","volume":"64","author":"Dohare","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_33","unstructured":"Platt, J. (2025, August 15). Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods. Available online: https:\/\/home.cs.colorado.edu\/~mozer\/Teaching\/syllabi\/6622\/papers\/Platt1999.pdf."},{"key":"ref_34","first-page":"101","article-title":"In Defense of One-Vs-All Classification","volume":"5","author":"Rifkin","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.jelectrocard.2023.12.009","article-title":"Validation of an automated artificial intelligence system for 12-lead ECG interpretation","volume":"82","author":"Herman","year":"2024","journal-title":"J. Electrocardiol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1093\/eurheartj\/ehx393","article-title":"2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force for the management of acute myocardial infarction in patients presenting with ST-segment elevation of the European Society of Cardiology (ESC)","volume":"39","author":"Ibanez","year":"2018","journal-title":"Eur. Heart J."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Fabricius Ekenberg, L., H\u00f8fsten, D.E., Rasmussen, S.M., M\u00f8lgaard, J., Hasbak, P., S\u00f8rensen, H.B.D., Meyhoff, C.S., and Aasvang, E.K. (2023). Wireless Single-Lead versus Standard 12-Lead ECG, for ST-Segment Deviation during Adenosine Cardiac Stress Scintigraphy. Sensors, 23.","DOI":"10.3390\/s23062962"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"290","DOI":"10.1097\/TME.0000000000000080","article-title":"The Value of Continuous ST-Segment Monitoring in the Emergency Department","volume":"37","author":"Bovino","year":"2015","journal-title":"Adv. Emerg. Nurs. J."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-Sell\u00e9s, M., and Marina-Breysse, M. (2023). Current and Future Use of Artificial Intelligence in Electrocardiography. J. Cardiovasc. Dev. Dis., 10.","DOI":"10.3390\/jcdd10040175"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Bartusik-Aebisher, D., Rog\u00f3\u017c, K., and Aebisher, D. (2025). Artificial Intelligence and ECG: A New Frontier in Cardiac Diagnostics and Prevention. Biomedicines, 13.","DOI":"10.3390\/biomedicines13071685"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Karim, S.R., Helseth, H.C., Baker, P.O., Keller, G.A., Meyers, H.P., Herman, R., and Smith, S.W. (2025). Artificial Intelligence Detection of Occlusive Myocardial Infarction from Electrocardiograms Interpreted as \u201cNormal\u201d by Conventional Algorithms. J. Pers. Med., 15.","DOI":"10.20944\/preprints202502.0281.v1"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chia, C.-C., and Syed, Z. (2014, January 24\u201327). Scalable noise mining in long-term electrocardiographic time-series to predict death following heart attacks. Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2623330.2623702"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"305","DOI":"10.3414\/ME15-05-0009","article-title":"Computational Electrocardiography: Revisiting Holter ECG Monitoring","volume":"55","author":"Deserno","year":"2016","journal-title":"Methods Inf. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.bja.2025.05.037","article-title":"Artificial intelligence for electrocardiographic diagnosis of perioperative myocardial ischaemia: A scoping review","volume":"135","author":"Kim","year":"2025","journal-title":"Br. J. Anaesth."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Makimoto, H., H\u00f6ckmann, M., Lin, T., Gl\u00f6ckner, D., Gerguri, S., Clasen, L., Schmidt, J., Assadi-Schmidt, A., Bejinariu, A., and M\u00fcller, P. (2020). performance of a convolutional neural network derived from an ECG database in recognizing myocardial infarction. Sci. Rep., 10.","DOI":"10.1038\/s41598-020-65105-x"}],"container-title":["Journal of Clinical Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/19\/6792\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:49:43Z","timestamp":1760035783000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2077-0383\/14\/19\/6792"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,25]]},"references-count":45,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["jcm14196792"],"URL":"https:\/\/doi.org\/10.3390\/jcm14196792","relation":{},"ISSN":["2077-0383"],"issn-type":[{"value":"2077-0383","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,25]]}}}