308 - AI IN CARDIOVASCULAR MEDICINE TODAY, AND TOMORROW

Saturday, October 25, 2025

8:30am - 9:30am ET

Room:307AB

Target Audience: Trainee (research), Trainee (clinical)

Co-Chair(s)

Sanaz Hatami, Dr. (she/her/hers)

Resident in Internal Medicine
University Of Calgary
University of Calgary
Sam Papernick, MD, MSc (he/him/his)

Resident Physician
University of Ottawa
University of Ottawa

Presenter(s)

Chi-Ming Chow, MD MSc FRCPC FCCS FASE

Professor
St. Michael's Hospital
RN

Rashmi Nedadur, MD MSc

Cardiac Surgeon
London Health Sciences Center
Western University
AS

Abhinav Sharma, MD, PhD

Assistant Professor
Research Institute of the McGill University Health Centre, McGill University
Research Institute McGill University Health Centre

Panelist(s)

Marvin Slepian, M.D., J.D.

Planning Committee Member(s)

Veronica Chan, MD (she/her/hers)

Internal Medicine Resident
Sanaz Hatami, Dr. (she/her/hers)

Resident in Internal Medicine
University Of Calgary
University of Calgary
Braeden Hill, BHSc

MD/PhD Student
University of Toronto
University of Toronto
Sam Papernick, MD, MSc (he/him/his)

Resident Physician
University of Ottawa
University of Ottawa

Workshop Description: Artificial intelligence is reshaping cardiovascular medicine and today’s trainees are poised to lead its next chapter. This session introduces how AI is being applied across cardiovascular care and research, from diagnostics and risk prediction to data-driven discovery. Learn from experts about real-world use cases, emerging innovations, and how trainees can get involved in this rapidly evolving field. Whether you're curious or already exploring AI, this session will provide the insight and direction to help you engage meaningfully with the future of cardiovascular medicine.

Learning Objectives:

At the end of this session participants will be able to:

to understand the current clinical applications of artificial intelligence in cardiovascular medicine, including diagnostic support, risk stratification, and workflow optimization;
to explore emerging innovations in AI-driven cardiovascular research, such as predictive modeling, machine learning in imaging, and integration with large-scale health data;
to identify opportunities for medical and research trainees to engage with AI in cardiovascular science, including research involvement, skill development, and interdisciplinary collaboration; and
to examine the key challenges and ethical considerations in implementing AI in clinical practice, including data privacy, algorithmic bias, and regulatory oversight.