Artificial intelligence (AI)-based ECG interpretation may significantly improve the early detection of certain heart attacks, particularly in cases where diagnosis remains challenging with conventional methods. This was demonstrated in a study presented at ESC Acute CardioVascular Care 2026, organised by the European Society of Cardiology.
In patients presenting with suspected acute coronary syndrome (ACS), clinicians typically rely on ECG changes, most notably ST elevation, to identify a severe form of heart attack known as ST-elevation myocardial infarction (STEMI). These cases require immediate intervention to restore blood flow.
However, for patients without ST elevation, diagnosis is less straightforward. Additional tests, such as cardiac biomarkers and coronary angiography, are often needed, which can delay treatment. According to Federico Nani, this diagnostic gap can have serious consequences. “Many patients without an ST elevation have an occlusive MI, but it can be difficult for clinicians to quickly and accurately recognize this, leading to delays in providing emergency treatment.”
AI as a decision-support tool
The study evaluated whether AI-based interpretation of the initial ECG could improve detection of occlusive myocardial infarction (MI) in patients without ST elevation. In this prospective, single-centre study, 1,490 patients with suspected ACS were assessed. Alongside standard diagnostic pathways, combining clinician ECG interpretation, troponin testing and, where necessary, angiography, the same ECGs were analysed using a CE-certified, smartphone-based AI algorithm.
The AI system identified occlusive MI in 108 patients (7%) and ruled it out in 1,382 cases. It achieved an accuracy of 84% in detecting occlusive MI, with a sensitivity of 77%, specificity of 99% and a negative predictive value of 98%. By contrast, conventional ECG interpretation correctly identified occlusive MI in 42% of cases.
Potential to accelerate treatment decisions
The findings suggest that AI-supported ECG analysis could play a valuable role in accelerating diagnosis and improving clinical decision-making, particularly in time-critical scenarios where early intervention is essential.
Nani emphasised the potential clinical impact: “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.”
He added that while further validation is needed, the results indicate that AI could complement existing tools and support earlier recognition and treatment of high-risk patients.
Growing role of AI in cardiovascular care
The study reflects a broader trend toward integrating AI into cardiovascular diagnostics and acute care pathways. As healthcare systems increasingly explore digital decision-support tools, technologies like AI-enhanced ECG interpretation may help reduce diagnostic delays and improve patient outcomes. The role of AI in cardiovascular disease management will be further explored at the upcoming ESC Congress 2026 in Munich, where it will be highlighted as a key theme.
Last month, researchers at the Mount Sinai Kravis Children’s Heart Centre developed an AI model that can use a standard ECG to assess which patients who have undergone surgery for tetralogy of Fallot require additional monitoring via MRI. By combining ECG and MRI data, the model learned to recognise subtle patterns that indicate changes in heart structure and function.
The study shows that AI-assisted ECGs can contribute to better risk stratification and more efficient use of scarce MRI capacity. At the same time, researchers emphasise that local validation is essential, as performance varies from hospital to hospital. The model is intended as a decision-support tool and not as a replacement for MRI, with the aim of improving the accessibility and personalisation of long-term cardiac care.
