A cardiologist and bioengineering researcher, Sanjiv Narayan has spent several decades working at the intersection of medicine, neuroscience, software engineering, and cardiac electrophysiology. A professor of medicine at Stanford University, he directs atrial fibrillation and electrophysiology research programs and has focused clinically on atrial fibrillation, atrial flutter, and complex ablation. His background includes medical and software engineering training in the United Kingdom, neuroscience research, and fellowships at UCLA, Harvard University, and Washington University School of Medicine. This experience connects directly to the growing role of technology in atrial fibrillation care, where digital health tools, monitoring systems, and data-driven methods are changing how clinicians detect, evaluate, and manage irregular heart rhythms. His work with bioengineering techniques and digital health research provides a factual context for examining these developments.
How Technology Is Influencing the Management of Atrial Fibrillation
Atrial fibrillation is an irregular, rapid, and potentially life-threatening heart rhythm that complicates blood flow from the heart, increasing the risks of developing strokes, blood clots, and heart failure. Exciting technology is reshaping how atrial fibrillation is detected and monitored, shifting this care effort from occasional, clinic-based evaluation to continuous, real-world tracking (1).
Atrial fibrillation is often intermittent and asymptomatic, making it challenging to detect using traditional tools such as an electrocardiogram, which captures irregular heart rhythms in limited recording windows, meaning episodes can easily go unnoticed. As such, a significant number of patients remain undiagnosed until complications, such as stroke and heart failure, occur.
Several digital health technologies are addressing this gap by enabling long-term, continuous monitoring (2). Wearable devices, mobile health applications, telemedicine, and artificial intelligence (AI) now enable the passive collection of heart rhythm data over extended periods. This shift is significant because atrial fibrillation does not always occur during clinical visits, so frequent monitoring increases the likelihood of detecting irregular episodes and provides an accurate picture of a patient’s heart rhythm.
Wearable devices, particularly smartwatches and fitness trackers, are important to this transformation. These devices rely on sensors, such as single-lead electrocardiography, to monitor one’s heart rate and rhythm in real-time. Researchers explain that smartwatch-based detection can achieve a significantly high specificity rate, making it effective in monitoring efforts. This accuracy level, coupled with the ease of use, allows individuals to continuously track their heart health without disrupting daily routines.
Beyond convenience, wearable technology enables early detection. Some devices can identify irregular heart rhythms before symptoms appear, prompting users to seek medical evaluation. Researchers argue that modern fitness trackers can detect signs of atrial fibrillation by analyzing heart rate variability and rhythm irregularities, approaching the diagnostic capabilities of electrocardiography (ECG) systems. Importantly, these devices are not intended to replace professional diagnosis, but rather serve as valuable screening tools that bridge the gap between patients and care providers.
Mobile health applications expand the capacities of wearable devices by providing platforms for data storage, visualization, and communication. These applications collect heart rhythm data and present it in a format that is easy for patients and clinicians to interpret. Some applications also generate alerts when abnormal patterns are detected, enabling timely medical intervention. Experts explain that integrating ECG-based systems with mobile applications can achieve high accuracy, improving specificity and sensitivity in detecting atrial fibrillation.
Telemedicine is another important innovation in detecting and monitoring atrial fibrillation. With interconnected devices, clinicians can monitor patients’ heart rhythms in real time without the need for frequent hospital visits. This is especially beneficial for those in remote and underserved regions. Continuous data transmission helps caregivers detect abnormalities early and promptly adjust treatment plans, reducing the burden on healthcare systems by minimizing hospital visits and efficiently utilizing resources.
AI is playing an increasingly important role in enhancing these technologies’ accuracy and predictive capabilities (3). AI algorithms analyze large volumes of heart rhythm data, identify important patterns, and distinguish between normal variations and clinically significant abnormalities. Modern AI-powered wearables, for instance, can detect atrial fibrillation and predict arrhythmia episodes before they occur, offering a critical window for preventive action.
Despite these advances, integrating technology in detecting and monitoring atrial fibrillation faces several challenges, including accuracy. Factors such as poor sensor contact and user error can affect data quality and lead to the generation of false-positive and false-negative results. Some studies highlight limitations in smartwatch-based screening, including variability in performance across different devices and populations, while other researchers argue that false alarms can evoke unnecessary anxiety, mandating additional testing and increasing healthcare costs.
There is a need to ensure that these technologies reach all people in need (4). Lastly, there are data privacy and security concerns in leveraging technology. Frequent monitoring efforts collect large amounts of sensitive health data, raising concerns about how this information is stored, shared, and protected. Complying with data protection regulations and implementing robust cybersecurity measures is, therefore, essential for the widespread adoption of technology in detecting and monitoring atrial fibrillation (5).
1. https://www.ahajournals.org/doi/full/10.1161/CIRCEP.124.012939
2. https://academic.oup.com/europace/article/27/5/euaf071/8100285?guestAccessKey=
3. https://www.ahajournals.org/doi/full/10.1161/CIR.0000000000001201
4. https://www.nature.com/articles/s41569-025-01184-5
5. https://www.nature.com/articles/s41746-025-01520-6
About Sanjiv Narayan
Dr. Sanjiv M. Narayan is a California cardiologist and Stanford University professor of medicine with over two decades of experience. His clinical focus has included atrial fibrillation, atrial flutter, and complex ablation, and his research has emphasized bioengineering, digital health, and arrhythmia medicine. He is a fellow of the American Heart Association, the American College of Cardiology, and the Royal College of Physicians of London.
