Case Report Demonstrates Potential Utility of AI in AFib Detection

In a recent case report published in Cureus, researchers discussed the benefits and challenges of artificial intelligence (AI) in healthcare while presenting a case of atrial fibrillation (AFib) detected by the patient’s smartwatch.

AI in healthcare has become a hot topic in recent years, inspiring conversations around the clinical, ethical, and financial implications of these algorithms in the industry. Some of the pros and cons of AI in healthcare that stakeholders are considering include the technology’s impact on the role of human healthcare workers, patient-provider relationships, clinician burnout, privacy and security, medical liability, and health equity. But many researchers are also investigating health AI’s potential use cases and patient care integration.

In this case report, the authors relay how a smartwatch detected AFib in a young patient, arguing that such an application of AI technology could aid in patient care more broadly.

The patient in question was a 25-year-old male with a history of depression and anxiety who presented in the emergency department (ED) complaining of palpitations and pressure-like chest discomfort for the past 24 hours. The patient reportedly sought medical attention after his smartwatch notified him of an irregular heart rhythm several times.

The patient’s family history did not indicate a predisposition for sudden cardiac death or inherited arrhythmias, but he indicated that he had received annual follow-ups from early childhood until the age of 18 for an aortic valve defect. However, he was medically cleared for the condition at age 18.

The authors further noted that the patient denied multiple constitutional symptoms of AFib, including cough, dizziness, focal weakness, numbness or tingling, fever, and night sweats, but did complain of slight shortness of breath on exertion. The patient indicated tobacco and occasional social alcohol use but denied using illicit drugs and stated that he had been binge drinking the weekend before his ED visit.

This led clinicians to conclude that the patient’s AFib was triggered by his alcohol consumption, which resulted in a case of holiday heart syndrome or alcohol-induced atrial arrhythmias. The patient was admitted for further monitoring and seen by a cardiologist, who recommended transesophageal echocardiography (TEE)-guided electrical cardioversion.

Following treatment, normal sinus rhythm was successfully restored, and the patient was declared medically stable for discharge, with a recommendation of alcohol cessation and monthly cardiology follow-up visits.

The authors posited that this is just one example of a smartwatch’s potential to improve AFib care, noting that the devices can record and analyze heart rate and rhythm in real time. These insights can then aid users with diagnosed and undiagnosed arrhythmias by continuously monitoring their vital signs and heart activity. Further, various stakeholders, including Apple, Fitbit, the University of Rochester Medical Center, and Johnson & Johnson, have leveraged the tech to help address AFib and other heart conditions.

Despite the potential of AI in this area, however, the authors highlighted some significant barriers to the technology’s incorporation into healthcare that must be addressed, such as “data governance and sharing, software failure concerns, job displacement, communication barriers between patients and machines, and accountability for medical errors,” alongside the possibility of data breaches that could compromise patient health information and jeopardize privacy.

While these pose major challenges for the potential integration of health AI, the researchers reiterated its value in managing AFib and associated complications through enhanced early detection. They concluded that once these obstacles are overcome, the benefits of AI in healthcare will be reaped for decades.