Collecting Data in the Electrophysiology Laboratory for Use in Artificial Intelligence Initiatives-Collecting Data in the Electrophysiology Laboratory for Use in Artificial Intelligence Initiatives: Challenges and Opportunities Powered by Johnson and Johnson

Collecting Data in the Electrophysiology Laboratory for Use in Artificial Intelligence Initiatives: Challenges and Opportunities Powered by Johnson and Johnson

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Video Summary

This expert panel discusses the challenges and progress in applying AI in electrophysiology (EP) labs for cardiac arrhythmia treatment. Key issues include the heterogeneity of data sources—such as CT scans, electrograms, mapping systems, and wearable devices—and the difficulty in pooling this diverse data into unified, standardized formats that AI can effectively utilize. Various institutions like Seoul National University, Stanford, and the University of Brussels are developing digital twin models, AI algorithms, and homegrown solutions to collect and integrate these multimodal data, though interoperability remains a major bottleneck.<br /><br />Speakers emphasize the need for standardized data structures, annotation protocols, and outcome definitions to enable meaningful AI insights and cross-institutional data sharing. Industry players like J&J MedTech are working on creating APIs and standards for aggregating complex data types. Data governance and ownership, including protection of proprietary information and regulatory expectations, also influence sharing practices, with some advocating for national or society-led initiatives to pool and democratize EP data.<br /><br />Ultimately, the panel highlights AI's potential to enhance procedure planning, predict treatment outcomes, and personalize therapy by understanding underlying arrhythmia mechanisms. However, foundational work remains to define relevant questions, curate high-quality data, and establish standards. The session concludes on the insightful point that AI should help not only identify who and how to treat but also importantly, who not to treat, optimizing patient care in electrophysiology.

Keywords

AI in electrophysiology

cardiac arrhythmia treatment

multimodal data integration

digital twin models

data standardization

interoperability challenges

data governance and ownership

personalized therapy in EP