Research interests
Interested in combining machine learning with noninvasive imaging to improve arrhythmia diagnosis and patient outcomes[1]
IN
Born in the former Yugoslavia and of Croatian descent; moved to Rochester, Minnesota as an exchange student in high school
Ivan Nenadic Wood is a cardiology physician-researcher whose work connects cardiac electrophysiology with biomedical engineering, machine learning, imaging, and wearable technologies. Duke’s profile lists him as a Fellow in the Cardiovascular Disease Fellowship, with a start year of 2023. He studied physics and mathematics at Saint Olaf College, earned a Ph.D. in Physiology and Biomedical Engineering at Mayo Clinic focused on cardiac shear wave elastography, and then attended Mayo for medical school. After residency at University of Michigan Hospital, he developed interests in electrophysiology and machine-learning applications to arrhythmia detection and wearable health technologies. He plans an academic cardiology career centered on translational research, noninvasive imaging, wearable monitoring, and device-based therapeutics. His 2026 public listings include an EHRA presentation and a scheduled HRX session on AI in electrophysiology.
Each topic is linked to its supporting source in Sources.
Training includes Scheduled speaker for a September 20, 2026 HRX session concerning clinical judgment and artificial intelligence in electrophysiology, Listed speaker for a presentation on deep learning from 1 million clinical ECGs using transform-domain representations for arrhythmia classification and ejection fraction estimation, Fellow, Cardiovascular Disease Fellowship at Duke, and Ph.D. in Physiology and Biomedical Engineering at the Mayo Clinic, specializing in cardiac shear wave elastography, plus 5 more records.
Explore education and trainingAs of 2026
As of 2026
As of 2023
Interested in combining machine learning with noninvasive imaging to improve arrhythmia diagnosis and patient outcomes[1]
Plans to develop medical technologies, particularly wearable health monitoring and device-based therapeutics[1]
Research collaborations applying machine learning to arrhythmia detection and wearable health technologies[1]