In a world in which AI models have superhuman abilities3 and health sensors are ubiquitous, there is a real potential to identify cardiac disease early via home monitoring with methods that are more continuous and less obtrusive than current screening approaches. This may identify people who can benefit from established therapies, and those who do not require medical care, to better utilize overburdened healthcare resources. Coupled with this opportunity is an obligation to rigorously test AI in real-world scenarios to demonstrate effectiveness before its use at scale4. Using a digital, remote study, we showed that an AI-ECG model can be adapted to effectively classify real-world Apple watch data. In addition to validating a specific model, we found that the use of a mobile-phone app is an affordable, fast and reliable method of collecting data, and that patients of all ages (ranging from 18 years to 90 years) remain highly engaged.

A potential limitation of our study is the cost of the Apple watch. Although these devices are becoming more affordable and their use is growing, their medical application may exacerbate healthcare inequities. However, distributing watches to clinics in underserved environments for use as a shared resource may permit very cost-effective screening. A second limitation is the limited racial diversity of the study population. Although the original 12-lead AI-ECG model has been found to be robust across races and ethnicities5, this needs to be assessed for the watch ECG as well.

We intend to expand the collection of ECGs from mobile devices made by any vendor that allows access to data (Apple watches were used due to open access to raw ECGs available via HealthKit; Apple was not aware of the study until its completion and provided no support) and to allow any patient to upload their data to the AI-ECG dashboard, to enable prospective screening of ventricular dysfunction and other diseases shown to be detectable by an AI-ECG in practice, and to facilitate access to care for patients in rural or resource-constrained areas.

Zachi I. Attia and Paul A. Friedman, Mayo Clinic, Rochester, MN, USA.