New Study Shows Artificial Intelligence Can Identify Heart Attacks Missed by ECG

Research presented at ESC Acute CardioVascular Care 2026 suggests AI-based ECG interpretation can more accurately detect occlusive myocardial infarctions (MI), especially in patients who do not show classic warning signs.

In cases of suspected acute coronary syndrome, doctors typically rely on ECG changes such as ST elevation to diagnose a severe heart attack known as STEMI. However, when this marker is absent, identifying an occlusive MI becomes far more challenging and can delay life-saving treatment.

Presenter Federico Nani explained: “Many patients without an ST elevation have an occlusive MI, but it can be difficult for clinicians to quickly and accurately recognise this, leading to delays in providing emergency treatment. We investigated whether AI-based interpretation of the initial ECG could improve the accuracy of detecting occlusive MIs in the absence of an ST elevation to optimise patient management.”

How did the AI model perform against traditional diagnosis?

The study analysed 1,490 patients with symptoms of acute coronary syndrome but no ST elevation. Alongside standard diagnostic procedures, a smartphone-based AI-ECG tool was used to interpret initial ECG readings.

The AI method ruled out occlusive MI in 1,382 patients and detected it in 108 cases, achieving an accuracy rate of 84 per cent. It demonstrated 77 per cent sensitivity, 99 per cent specificity, and a 98 per cent negative predictive value, with minimal false results.

In contrast, conventional ECG interpretation by clinicians correctly identified occlusive MI in just 42 per cent of cases, highlighting the potential of AI to bridge a critical diagnostic gap.

What does this mean for the future of cardiac care?

The findings indicate that AI could become a powerful support tool in emergency settings, helping doctors make faster and more accurate decisions in high-risk cases.

Doctor Nani concluded: “This simple, accessible AI-based approach demonstrated superior accuracy in identifying and excluding occlusive MI compared with conventional diagnostic pathways in patients without an ST elevation. The results of our single-centre study require further validation, but these findings suggest that AI ECG interpretation is a valuable addition to existing decision-making tools to improve early recognition and timely, effective treatment.”

The broader potential of AI in cardiovascular medicine will be further explored at the upcoming ESC Congress 2026 in Munich, where it has been chosen as a central theme.

(With inputs from ANI)