How Can AI Help Overcome Challenges in Electrophysiology?-How Can AI Help Overcome Challenges in Electrophysiology?

Video Transcription

Video Summary

In this panel discussion, experts in electrophysiology, AI development, and cybersecurity explore the challenges and opportunities of applying AI to atrial fibrillation (AF) management. They highlight that unlike language models, AF's complexity lacks clear “building blocks,” making AI model development difficult. Integrating multimodal data—such as imaging, electrophysiology, and wearable device data—can help characterize AF phenotypes more precisely, potentially distinguishing between benign and severe forms. Continuous, longitudinal data from wearables are seen as crucial for personalized treatment and understanding AF progression. The discussion emphasizes collaboration between clinicians and AI experts to formulate meaningful clinical questions guiding AI development. Cybersecurity and regulatory compliance are critical to safely enabling large-scale data sharing needed for AI research. The FDA’s evolving mandates now require robust cybersecurity throughout a device lifecycle, reflecting a global trend. Panelists advocate for increased data openness and interoperability, particularly integrating diverse patient data into mapping systems to enhance procedural planning and outcomes. They stress the importance of clinical relevance, explainability, and standardization in AI research, noting current underreporting in studies. Lastly, they call for integrating AI education early in medical training to prepare future physician-engineers, emphasizing that AI will increasingly impact clinical decision-making. Overall, the panel underscores both the promise of AI and the need for greater data, collaboration, transparency, and trust.

Keywords

atrial fibrillation

artificial intelligence

electrophysiology

multimodal data integration

wearable devices

cybersecurity

clinical collaboration

AI education