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Session Evaluation

BASIC SCIENCE

EDUCATION

RHYTHM

HEART FAILURE

EARLY CAREER INVOLVEMENT@CAIC

230 - ARTIFICIAL INTELLIGENCE AND DIGITAL TOOLS: NEW OPPORTUNITIES TO IMPROVE HEALTHCARE QUALITY

Friday, October 25, 2024
11:00 AM – 12:00 PM PT
Room: 111-112
Early Career Involvement
Workshop Description: In 2024, there are increasing opportunities to incorporate artificial intelligence (AI) into cardiovascular care. In this year’s AI workshop, we’ll explore how AI can be leverage across domains to improve healthcare quality. Specifically, invited speakers will discuss how AI can be leveraged to improve quality in echocardiography, heart failure management, arrhythmia diagnosis, and coronary angiography interpretation. Finally, we will discuss broadly how AI (including large language models) can be implemented in physician offices to optimize patient care.

This session will feature a multidisciplinary panel with experience in working with artificial intelligence, and including members from the Canadian Society of Echocardiography, Canadian Heart Failure Society, Canadian Heart Rhythm Society, and Canadian Association of Interventional Cardiology. The session will include the following topics:
1) Automated Echocardiographic Interpretation: Insights from an AI Echo Lab (Teresa Tsang)
2) Heart Failure Management: How AI can predict exacerbations and augment heart failure care (Chris McIntosh)
3) Finding the Needle in the Haystack: AI to Detect Inherited Arrhythmias (Christopher Cheung)
4) Automated Angiographic Interpretation and Estimating Ejection Fraction (Pascal Theriault-Lauzier)
5) Large Language Models to Optimize the Physician Office and Patient Care (Pavel Antiperovitch)

Invited speakers have expertise across these domains. In the first section, participants will learn about how automated echocardiographic interpretation can supplement conventional reporting to improve quality and accuracy. The second session will feature emerging evidence to support AI algorithms to predict exacerbations and improve heart failure care. In the third session, participants will learn about AI algorithms that can be used to detect rare inherited arrhythmia syndromes. The fourth session will focus on AI use in the catheterization lab, including automated angiographic interpretation and estimation of ejection fraction from angiographic images. The fifth session will conclude the workshop, focusing on how we can use AI to optimize care in physician offices, and will discuss how large language models can be incorporated, and also review privacy and security implications of these methods.

This workshop will be of interest to CCC audience, including general cardiologists, academic cardiologists, scientists, AI researchers, and trainees. We anticipate that there will be discussion on both the AI innovations occurring across various disciplines/subspecialties, along with substantial interest on how AI innovations can be applied/used in the general cardiology office (i.e. large language models in the physician office).

Interactive and Engagement Activities Description: Each speaker will provide an approximate 6-8 minute presentation on the topic assigned. There will be an allotted 13-15 minute panel discussion/Q&A period (time permitting) at the end of the session. Speakers will also be encouraged to use live polling to increase the interactivity of the session.

Learning Objectives:

At the end of this session participants will be able to:
  • delve into novel AI approaches for optimizing heart failure management, and discuss opportunities to predict heart failure exacerbations and allow for early intervention;
  • recognize the emerging opportunities of artificial intelligence integration into echocardiographic interpretation, point-of-care ultrasound, and coronary angiography interpretation;
  • discuss the emerging role of AI for rare disease diagnosis, focusing on inherited arrhythmia syndromes such as Long QT syndrome;
  • explore how to incorporate AI and large language models in the physician office to optimize patient care; and
  • understand the privacy and security implications of large language models, and how they can be incorporated in a safe and effective manner to optimize patient care.
Disclosure(s):
Chris McIntosh, PhD: No financial relationships to disclose
Pascal Theriault-Lauzier, MD FRCPC: No financial relationships to disclose
Teresa S.M Tsang, MD, FRCPC, FACC, FASE: Bayer: Consultant/Advisory Board (Ongoing), Research Grant (includes principal investigator, collaborator or consultant and pending grants as well as grants already received) (Ongoing), Speaker/Honoraria (Ongoing); Boehringer-Ingelheim: Speaker/Honoraria (Ongoing); Bristol Myers Squibb: Speaker/Honoraria (Ongoing); Pfizer: Consultant/Advisory Board (Ongoing), Speaker/Honoraria (Ongoing); Philips: Consultant/Advisory Board (Ongoing); Sanofi: Consultant/Advisory Board (Ongoing), Research Grant (includes principal investigator, collaborator or consultant and pending grants as well as grants already received) (Ongoing), Speaker/Honoraria (Ongoing); Servier: Speaker/Honoraria (Ongoing); Takeda: Speaker/Honoraria (Ongoing)
Robert Avram, MD MSc FRCPC: No financial relationships to disclose