AIMM: Artificial Intelligence for MultiMorbidity Beyond arrhythmias: Artificial intelligence enabled electrocardiogram for multi-morbidity

An artificial intelligence system is learning to read electrocardiograms (ECGs) for signs of disease far beyond the heart’s rhythm. ECGs are cheap, fast, and widely used, but doctors typically only look at them for obvious arrhythmias. The problem is that many other conditions—such as kidney disease, diabetes, or anaemia—can leave subtle electrical fingerprints on the heart’s activity that a human eye cannot spot. This project will train AI to detect those hidden signals, turning a routine heart test into a multi-disease screening tool. If it works, a single ECG could flag early signs of several chronic illnesses at once, without extra tests or appointments. That would matter most for people with multiple long-term conditions—multi-morbidity—who currently face fragmented care and repeated hospital visits. The impact would be on medical diagnostics: a faster, cheaper way to catch problems earlier, especially in primary care or community settings where specialist equipment is scarce. This is applied research with a clear practical target. The abstract does not provide specific figures on accuracy, patient numbers, or cost savings, so no numerical claims are made here.