Innovation Partner Roundtable – Collecting Data in the Electrophysiology Laboratory for Use in Artificial Intelligence Initiatives: Challenges and Opportunities – Powered by Johnson & Johnson MedTech

September 04, 2025 | 02:45 PM (EDT) - 03:30 PM (EDT)

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Speakers
Alexandre Almorad
Cardiac Electrophysiologist | Director of the Arrhythmia Unit
Brussels University Hospital St Pierre | Brussels Heart Rhythm Management Center
Tina Baykaner, MD, MPH
Assistant Professor
Department of Internal Medicine | Division of Cardiovascular Medicine and Electrophysiology | Stanford University
Yoram Chmiel
Director of R&D Insights and Digital Collaborations
J&J MedTech
Kalyanam Shivkumar
Professor & Director, Center for Interventional Programs
UCLA Health System, UCLA Cardiac Arrhythmia Center & EP Programs
Hee Tae Yu
Cardiac Electrophysiologist, Associate Professor of Medicine
Yonsei University College of Medicine
Moderator
Nassir Marrouche
Director of Heart and Vascular Institute
Tulane University School of Medicine

Innovation Partner Roundtable - Collecting Data in the Electrophysiology Laboratory for Use in Artificial Intelligence Initiatives: Challenges and Opportunities - Powered by Johnson & Johnson MedTech

Roundtable 1
September 04, 2025 | 02:45 PM (EDT) - 03:30 PM (EDT)

As AI begins to transform the EP landscape, high-quality data collection in the EP laboratory has become a foundational requirement. However, the process of collecting, structuring, labeling, and integrating multimodal EP data—including signals, imaging, annotations, and procedural metadata—presents significant technical, regulatory, and operational challenges. This roundtable will convene EP clinicians, data scientists, digital health leaders, and industry innovators to explore how data generated during routine EP procedures can be leveraged to power AI algorithms. Topics will include interoperability between EP lab systems, data ownership and governance, strategies for annotation and ground truth creation, privacy-preserving data sharing, and collaborative AI development between health systems and industry. Panelists will also showcase early successes and lessons learned from existing AI initiatives in arrhythmia detection, ablation guidance, and post-procedure outcomes prediction.

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