{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T05:12:59Z","timestamp":1783660379589,"version":"3.55.0"},"reference-count":67,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Current acute coronary syndrome (ACS) care relies on sequential, single-modality diagnostics, in which the electrocardiogram, the troponin trajectory, and the coronary angiogram are interpreted independently rather than as a joint signal. This narrative review maps rather than pools the evidence. We selectively searched PubMed, EMBASE, Cochrane CENTRAL, and Web of Science (January 2015\u2013February 2026); study selection was performed by a single reviewer, without duplicate screening, a PRISMA flow diagram, or a formal risk-of-bias assessment. The three key findings are as follows: A machine learning-enabled electrocardiogram (ECG) for diagnosing occlusion due to myocardial infarction achieved an AUC of 0.938 (95% CI = 0.924\u20130.951) on data not seen during training and correctly diagnosed 42% of patients that expert interpreters missed. A machine learning-enabled high-sensitivity troponin interpretation method, CoDE-ACS, reported an AUC of 0.953 and increased the number of patients ruled out at initial evaluation from 27% to 61%. Angiographically derived physiological methods produced conflicting results\u2014quantitative flow ratios reduced major adverse cardiovascular events (MACE) in the FAVOR III China trial (HR 0.65), but in FAVOR III Europe the angiography-derived approach did not prove non-inferior to FFR; if anything, QFR guidance led to more events (6.7% vs. 4.2%, an event rate about 60% higher in the QFR arm; HR 1.63; 95% CI 1.11\u20132.41). There was no difference between FFR-angio and FFR in the ALL-RISE trial. These are diagnostic-accuracy and prognostic-association findings; no trial has yet shown that AI-guided ACS care reduces death, reinfarction, or ischemia-driven revascularization.<\/jats:p>","DOI":"10.3390\/diagnostics16132046","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T16:56:55Z","timestamp":1782838615000},"page":"2046","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["From Electrocardiography to the Catheterization Laboratory: A Multimodal Artificial Intelligence Framework for Acute Coronary Syndrome Detection and Risk Stratification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7882-9255","authenticated-orcid":false,"given":"Marek","family":"Tomala","sequence":"first","affiliation":[{"name":"Faculty of Medicine and Health Sciences, University of Applied Sciences in Nowy S\u0105cz, 33-300 Nowy S\u0105cz, Poland"},{"name":"Center for Invasive Cardiology, Electrotherapy, and Angiology, Clinical Research Center Intercard, Al. Beliny-Pra\u017cmowskiego 60\/4, 31-514 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2009-1216","authenticated-orcid":false,"given":"Maciej","family":"K\u0142aczy\u0144ski","sequence":"additional","affiliation":[{"name":"Department of Mechanics and Vibroacoustics, Faculty of Mechanical Engineering and Robotics, AGH University of Krakow, Al. Mickiewicza 30, 30-059 Krakow, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1804","DOI":"10.1038\/s41591-023-02396-3","article-title":"Machine Learning for ECG Diagnosis and Risk Stratification of Occlusion Myocardial Infarction","volume":"29","author":"Bouzid","year":"2023","journal-title":"Nat. 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