سامانه همایش های دانشگاه آزاد اسلامی واحد نجف آباد

صفحه اصلی / اولین همایش بین المللی تحول خدمات سلامت: تلفیق مراقبت، درمان، بازتوانی و امیدبخشی

Integration of Artificial Intelligence and Telemedicine in the Diagnosis and Remote Monitoring of Atrial Fibrillation: A Review of Current Evidence

نویسندگان :

MohammadHossein Asghari¹ Samira Shariati Najafabadi²

1- دانشگاه آزاد اسلامی واحد نجف آباد 2- دانشگاه آزاد اسلامی واحد نجف آباد

کلمات کلیدی :

Atrial Fibrillation،Artificial Intelligence،Telemedicine،Wearable Electronic Devices،Electrocardiography

چکیده :

Background: Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia and is associated with substantial risks of heart failure. Artificial intelligence (AI) has showed potential to improve the AF detection (1, 2). Meanwhile, telemedicine has facilitated continuous patient remote monitoring and may reduce unnecessary hospitalizations (3, 4). Existing studies haven’t reviewed these technologies comprehensively. This review aimed to evaluate existing evidence on integration of AI and telemedicine in diagnosing and remote monitoring AF, to identify gaps in the combination of these two approaches. Methods: A narrative review was conducted by searching major scientific databases, such as PubMed. The search was performed using keywords related to atrial fibrillation, artificial intelligence, and telemedicine. Findings: AI algorithms, specifically deep learning models applied to data from wearable Electrocardiograph devices, have shown high sensitivity for AF diagnosis and have been superior to traditional methods in some studies (1, 2, 5). Telemedicine virtual wards have shown potential for reducing hospitalizations (3). Also, AI-assisted remote monitoring of implantable cardiac devices has reduced the time to diagnosis (4). Despite these advances, most telemedicine models do not fully use AI-based predictive tools. Moreover, AI-based studies have not focused enough on the real-world implementation of telemedicine, highlighting the need for integrated care models (5). Conclusion: Integrating the diagnostic capabilities of AI with telemedicine-based monitoring may improve early detection and outpatient management of AF. Future studies should focus on prospective evaluation of AI-telemedicine platforms in actual clinical settings.