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Health·The Trace·Automated dual reading·Published 2026-08-05

STEMI diagnosis by physician assistants using Queen of Hearts AI ECG interpretation

Source article: AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study

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 tim…

TRV-2026-0651Peer-reviewedPermanent record — cite & verify
Trace impact reading

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P 71The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 71The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
AI-Assisted Electrocardiogram Interpretation Improves ST-Elevation Myocardial Infarction Diagnostic Accuracy Among Advanced Practice Providers: A Prospective Randomized Crossover Study

Doctors Hospital from the Southwest 1 by Sixflashphoto. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

In a prospective randomized crossover study at Carl R. Darnall Army Medical Center, 21 certified physician assistants interpreted 50 de-identified 12-lead ECGs with and without Queen of Hearts AI software by PMcardio. Diagnostic accuracy rose from 79.0% to 92.9% with AI, with sensitivity 95.4% versus 82.5% and specificity 90.5% versus 75.6%, and interrater agreement improved from kappa 0.58 to 0.86.

The improvement matters because timely STEMI recognition is critical where evacuation may be delayed, and advanced practice providers often serve as frontline interpreters. The trade-off was longer decision time, 18.8 versus 13.0 minutes cumulatively, raising questions about workflow impact, generalizability beyond a single center and 50 ECGs, and performance in live operational environments with more variable ECG quality and clinical pressures.

Main points
  • Prospective randomized multi-reader multi-case crossover study at Carl R. Darnall Army Medical Center, Fort Hood, TX, with 21 PA-Cs as their own controls.
  • Each participant interpreted 50 de-identified 12-lead ECGs (25 STEMI, 25 non-STEMI) with and without Queen of Hearts AI software by PMcardio.
  • Primary outcome was diagnostic accuracy; secondary outcomes were confidence on 5-point Likert scale and time-to-decision, analyzed with paired t-tests and random-effects crossover model.
  • Confidence improved with AI: mean Likert total 46.8 vs 56.9, lower score indicates higher confidence, with significant improvement reported.
Gain

AI-assisted interpretation using Queen of Hearts software improved STEMI diagnostic accuracy, sensitivity, specificity, and interrater agreement among certified physician assistants interpreting 12-lead ECGs.

Problem

AI assistance increased cumulative time-to-decision for ECG interpretation, adding an average of 14.7 seconds per ECG strip.

The rundown

The study used a crossover design where each of the 21 PA-Cs interpreted the same 50 ECGs both with and without AI, with analysis facilitated by the Baylor University Statistical Consulting Center.

Authors reported that although time increased, the mean increase of approximately 14.7 seconds per ECG is unlikely to be clinically meaningful, and concluded findings support integration into frontline and operational settings to enhance timely and accurate STEMI recognition.

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