Researchers at Mayo Clinic have used artificial intelligence (AI) to evaluate patients' electrocardiograms (ECGs) in a targeted strategy to screen for atrial fibrillation, a common heart rhythm disorder. Atrial fibrillation is an irregular heartbeat that can lead to blood clots that may travel to the brain and cause a stroke, but it is largely underdiagnosed. In the digitally-enabled, decentralized study, AI identified new cases of atrial fibrillation that would not have come to clinical attention during routine care. Earlier research had already developed an AI algorithm to identify patients with a high likelihood of previously unknown atrial fibrillation. "We believe that atrial fibrillation screening has great potential, but currently the yield is too low and the cost is too high to make widespread screening a reality," says Peter Noseworthy, M.D., a cardiac electrophysiologist at Mayo Clinic and lead author of the study.

This editorial refers to'Identification of undiagnosed atrial fibrillation using a machine learning risk-prediction algorithm and diagnostic testing (PULsE-AI) in primary care: a multi-centre randomized controlled trial in England', by N.R. Hill et al., https://doi.org/10.1093/ehjdh/ztac009. To see things in the seed, that is genius. Undiagnosed atrial fibrillation (AF) is an important cause of stroke.1 AF screening may enable prompt detection of AF and initiation of oral anticoagulation (OAC) to prevent stroke.2 The 2007 SAFE trial reported a roughly 50% increase in AF diagnosis with screening individuals aged 65 years using electrocardiography (ECG) with or without pulse palpation,3 resulting in a Class I recommendation from the European Society of Cardiology4 and the Cardiac Society of Australia and New Zealand5 for AF screening using ECG among individuals aged 65 years. However, more recent studies suggest that mass screening may not be effective.6,7