{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T17:46:31Z","timestamp":1785865591927,"version":"3.56.0"},"reference-count":11,"publisher":"Oxford University Press (OUP)","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"Abstract<\/jats:title>\n \n Introduction<\/jats:title>\n Timely and accurate diagnosis of ST-elevation myocardial infarction (STEMI) is critical in military operational environments where evacuation may be delayed. Although artificial intelligence (AI) electrocardiogram (ECG) tools have demonstrated high diagnostic performance, their effectiveness among advanced practice providers (APPs) remains untested. This study evaluated whether AI-ECG interpretation by Queen of Hearts (QoH) AI software by PMcardio improves STEMI diagnostic accuracy, clinician confidence, and time-to-decision among APPs.<\/jats:p>\n <\/jats:sec>\n \n Materials and Methods<\/jats:title>\n This prospective, randomized, multi-reader, multi-case crossover study enrolled 21 certified physician assistant (PA-Cs) at Carl R. Darnall Army Medical Center, Fort Hood, TX. Participants interpreted 50 de-identified 12-lead ECGs (25 STEMI, 25 non-STEMI) under 2 conditions: with and without AI assistance. Each participant served as their own control. The primary outcome was diagnostic accuracy. Secondary outcomes included confidence (5-point Likert scale) and time-to-decision. Data were analyzed using paired t-tests and a random-effects crossover model, with analysis facilitated by the Baylor University Statistical Consulting Center.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n AI-ECG interpretation significantly improved diagnostic accuracy (92.9% vs. 79.0%; P &lt; .001; Cohen d\u2009=\u20091.68). With AI assistance, sensitivity was 95.4%, and specificity was 90.5%, compared to 82.5% and 75.6% without AI, respectively. Interrater agreement improved from moderate (\u03ba\u2009=\u20090.58) to excellent (\u03ba = 0.86). Confidence was significantly better with AI (mean Likert total 46.8 vs. 56.9; P &lt; .001; d\u2009=\u2009\u22121.02, lower score indicates higher confidence). Cumulative time-to-decision was significantly longer with AI (18.8 vs. 13.0 minutes; P &lt; .001; d\u2009=\u20091.13). The average increase in time per test strip was 14.7 seconds.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n AI-assisted ECG interpretation significantly improved STEMI diagnostic accuracy and clinician confidence among PA-Cs. Although AI increased time-to-decision, the mean increase of approximately 14.7 seconds per ECG is unlikely to be clinically meaningful. These findings support the integration of AI-ECG decision-support tools into frontline and operational settings to enhance timely and accurate STEMI recognition.<\/jats:p>\n <\/jats:sec>","DOI":"10.1093\/milmed\/usag365","type":"journal-article","created":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T11:40:00Z","timestamp":1784893200000},"source":"Crossref","is-referenced-by-count":0,"title":["AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study"],"prefix":"10.1093","author":[{"given":"Richard","family":"Palomino","sequence":"first","affiliation":[{"name":"Department of Emergency Medicine, Carl R. Darnall Army Medical Center , Fort Hood, TX 76544,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5099-8866","authenticated-orcid":false,"given":"Matthew J","family":"Perdue","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Carl R. Darnall Army Medical Center , Fort Hood, TX 76544,","place":["United States"]},{"name":"Robbins College of Health and Human Sciences, Baylor University , Waco, TX 76706,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9921-1948","authenticated-orcid":false,"given":"Alec","family":"Pawlukiewicz","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Carl R. Darnall Army Medical Center , Fort Hood, TX 76544,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6825-860X","authenticated-orcid":false,"given":"Lindsay","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Carl R. Darnall Army Medical Center , Fort Hood, TX 76544,","place":["United States"]},{"name":"Robbins College of Health and Human Sciences, Baylor University , Waco, TX 76706,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-2387-2026","authenticated-orcid":false,"given":"Christopher","family":"Mitchell","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Carl R. Darnall Army Medical Center , Fort Hood, TX 76544,","place":["United States"]},{"name":"Department of Military and Emergency Medicine, Uniformed Services University of Health Sciences , Bethesda, MD 20814,","place":["United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,8,4]]},"reference":[{"key":"2026080412441146300_usag365-B1","author":"U.S. Army Medical Command","year":"2026"},{"key":"2026080412441146300_usag365-B2","author":"U.S. Department of Defense","year":"2026"},{"key":"2026080412441146300_usag365-B3","author":"MEDCoE Lessons Learned","year":"2025"},{"key":"2026080412441146300_usag365-B4","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1097\/AJP.0b013e3181f06b06","article-title":"Noncardiac chest pain during war","volume":"27","author":"Cohen","year":"2011","journal-title":"Clin J Pain"},{"key":"2026080412441146300_usag365-B5","doi-asserted-by":"crossref","first-page":"e1309","DOI":"10.1161\/CIR.0000000000001328","article-title":"2025 ACC\/AHA\/ACEP\/NAEMSP\/SCAI guideline for the management of patients with acute coronary syndromes: a report of the American College of Cardiology\/American Heart Association joint committee on clinical practice guidelines","volume":"151","author":"Rao","year":"2025","journal-title":"Circulation"},{"key":"2026080412441146300_usag365-B6","doi-asserted-by":"crossref","first-page":"1461","DOI":"10.1038\/s41591-024-02961-4","article-title":"AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial","volume":"30","author":"Lin","year":"2024","journal-title":"Nat Med"},{"key":"2026080412441146300_usag365-B7","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1016\/j.ijcard.2022.06.012","article-title":"Artificial intelligence versus physicians on interpretation of printed ECG images: diagnostic performance of ST-elevation myocardial infarction on electrocardiography","volume":"363","author":"Choi","year":"2022","journal-title":"Int J Cardiol"},{"key":"2026080412441146300_usag365-B8","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ajem.2025.07.061","article-title":"Accuracy of cath lab activation decisions for STEMI-equivalent and mimic ECGs: physicians vs. AI (queen of hearts by PMcardio)","volume":"97","author":"Shroyer","year":"2025","journal-title":"Am J Emerg Med"},{"key":"2026080412441146300_usag365-B9","doi-asserted-by":"crossref","first-page":"e2376","DOI":"10.1093\/milmed\/usaf215","article-title":"Army medic performance in trauma sonography: the impact of artificial intelligence assistance in focused assessments with sonography in trauma\u2014a prospective randomized controlled trial","volume":"190","author":"Hartline","year":"2025","journal-title":"Mil Med"},{"key":"2026080412441146300_usag365-B10","doi-asserted-by":"crossref","first-page":"419","DOI":"10.1016\/j.cjco.2020.11.013","article-title":"Effect of real-time physician oversight of prehospital STEMI diagnosis on ECG-inappropriate and false positive catheterization laboratory activation","volume":"3","author":"Boivin-Proulx","year":"2021","journal-title":"CJC Open"},{"key":"2026080412441146300_usag365-B11","doi-asserted-by":"crossref","first-page":"4717","DOI":"10.1093\/eurheartj\/ehab649","article-title":"Application of artificial intelligence to the electrocardiogram","volume":"42","author":"Attia","year":"2021","journal-title":"Eur Heart J"}],"container-title":["Military Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/milmed\/advance-article-pdf\/doi\/10.1093\/milmed\/usag365\/70481596\/usag365.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/milmed\/advance-article-pdf\/doi\/10.1093\/milmed\/usag365\/70481596\/usag365.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T16:44:21Z","timestamp":1785861861000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/milmed\/advance-article\/doi\/10.1093\/milmed\/usag365\/8751211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,4]]},"references-count":11,"URL":"https:\/\/doi.org\/10.1093\/milmed\/usag365","relation":{},"ISSN":["0026-4075","1930-613X"],"issn-type":[{"value":"0026-4075","type":"print"},{"value":"1930-613X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,4]]},"article-number":"usag365"}}