Artificial Intelligence in Clinical Cardiac Electrophysiology

In this episode of The EP Edit, we’re featuring a discussion on the role of artificial intelligence, machine learning, and deep learning in cardiac electrophysiology. Arun R. Sridhar, MBBS, MPH, and Patrick M. Boyle, PhD, from the University of Washington will be discussing current applications, future directions, as well as their recent article in EP Lab Digest. Dr Boyle is an assistant professor in the Department of Bioengineering at the University of Washington, and Dr Sridhar is a cardiac electrophysiologist and assistant professor at the University of Washington School of Medicine.

You can now listen to this episode on Spotify and Apple Podcasts.

For more information about specific topics covered in this podcast, please see the following timestamps:

4:56: Scaling of arrhythmia care using a combination of consumer electronics with robust AI-based algorithms for arrhythmia discrimination.

6:40: The need for bilinguality among AI researchers, where clinicians understand the basic concepts and can relate to the model building, and bioengineers understand the clinical importance and can successfully collaborate with each other.

10:05: Publication bias in research related to AI in medicine.

12:47: Improving generalizability of AI algorithms by advancements in data sharing techniques, which enables testing the algorithm in varied datasets, as well as anonymization techniques.

16:28: Challenges with FDA regulation of AI algorithms.

18:33: The role of interdisciplinary collaborations in building trustworthy AI models.

19:11: Discussion on importance of explainability in clinical AI.

To view the entire podcast transcript, click here. (transcripts edited by Jodie Elrod)