The Lead Episode 83: A Discussion of Artificial Intelligence Age Prediction Using Electrocardiogram Data: Exploring Biological Age Differences-The Lead Episode 83 Speaker Information

The Lead Episode 83 Speaker Information

Pdf Summary

The discussion, hosted by Deepthy Varghese, MSN, ACNP, FNP from Northside Hospital, centers on a research article titled "Artificial Intelligence Age Prediction Using Electrocardiogram Data: Exploring Biological Age Differences," published in the Heart Rhythm Journal. The article includes authors Shaun Evans, MD, Sarah A. Howson, MD, and several others, and investigates the potential of using artificial intelligence (AI) to predict age based on electrocardiogram (ECG) data, examining how this might reflect biological age differences.<br /><br />Today’s discussion also features contributions from Tina Baykaner, MD, MPH from Stanford University, and Janet K. Han, MD, FHRS from Northwestern University and the VA Greater Los Angeles Healthcare System/UCLA. The contributors bring their expertise to the table, enhancing the discussions surrounding the implications and future applications of the study's findings.<br /><br />The focal point of the article is the use of AI to gauge an individual’s biological age through ECG, a method that could offer insights into age-related health issues by potentially highlighting discrepancies between chronological and biological age. Such insights might underscore the importance of personalized healthcare strategies based on AI-derived age assessments.<br /><br />In terms of disclosures, both Tina Baykaner and Deepthy Varghese have no significant disclosures, but they have been involved in research funded by the NIH and have consulting roles with companies like Medtronic and others. Meanwhile, Janet K. Han has engaged in speaking, teaching, and consulting roles with Medtronic and iRhythm Technologies.<br /><br />The article and subsequent discussion underscore the promising future of AI in medical diagnostics and its role in potentially redefining approaches to health assessments through advanced technologies like ECG.

Keywords

Artificial Intelligence

Age Prediction

Electrocardiogram

Biological Age

Heart Rhythm Journal

Personalized Healthcare

Medical Diagnostics

AI Applications

ECG Data

Healthcare Technology