A new artificial intelligence (AI) model called AIRE has shown the ability to detect heart conditions from a single electrocardiogram (ECG) in less than two seconds. Developed by British researchers, including Dr. Ahmed El-Medany from Imperial College London and Dr. Fu Siong Ng, the AI is designed to identify subtle signals in ECG traces that are not visible to human eyes. This technology has the potential to significantly speed up the detection of heart diseases, improving early diagnosis and patient outcomes. The AIRE model consists of two parts, each targeting different heart conditions. The first part, which focuses on valve disease, was published in the European Heart Journal in November 2025. It was trained using nearly a million ECG and echocardiogram pairs from Zhongshan Hospital in Shanghai and validated on over 34,000 patients from the Beth Israel Deaconess Medical Center in Boston. The model achieved a C-index of 69 to 79%, indicating a moderate to high ability to distinguish between patients with and without significant valve leakage. The second part of the AI, presented at the European Society of Cardiology (ESC) 2026 conference, targets heart failure. It was trained on 1.6 million ECGs from Brazil and several million from the United States. In testing, the model detected 81% of heart failures and 90% of valve diseases in a group of around 67,000 patients. While these results are promising, they have not yet been submitted for peer review, which is a standard process for validating scientific research. Imperial College London has launched a spin-out company, Cardiovolt.ai, to develop and commercialize the AI technology. A prospective study is currently underway in the National Health Service (NHS) in London and Bristol, testing portable ECG devices in real-world conditions. Dr. El-Medany refers to the system as "superhuman AI," aiming for compact ECG readers that can be used directly by general practitioners and emergency physicians. While the tool is not intended to replace more detailed tests like echocardiography, it could help prioritize patients who need further examination, reducing waiting times and improving care efficiency. Specific plans for large-scale deployment have not yet been announced.