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Review
. 2024 Jun 18;83(24):2487-2496.
doi: 10.1016/j.jacc.2024.03.401. Epub 2024 Apr 7.

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week

Sneha S Jain  1 , Pierre Elias  2 , Timothy Poterucha  3 , Michael Randazzo  4 , Francisco Lopez Jimenez  5 , Rohan Khera  6 , Marco Perez  1 , David Ouyang  7 , James Pirruccello  8 , Michael Salerno  1 , Andrew J Einstein  3 , Robert Avram  9 , Geoffrey H Tison  8 , Girish Nadkarni  10 , Vivek Natarajan  11 , Emma Pierson  12 , Ashley Beecy  13 , Deepa Kumaraiah  14 , Chris Haggerty  15 , Jennifer N Avari Silva  16 , Thomas M Maddox  17
Affiliations
Review

Artificial Intelligence in Cardiovascular Care-Part 2: Applications: JACC Review Topic of the Week

Sneha S Jain et al. J Am Coll Cardiol. .

Abstract

Recent artificial intelligence (AI) advancements in cardiovascular care offer potential enhancements in effective diagnosis, treatment, and outcomes. More than 600 U.S. Food and Drug Administration-approved clinical AI algorithms now exist, with 10% focusing on cardiovascular applications, highlighting the growing opportunities for AI to augment care. This review discusses the latest advancements in the field of AI, with a particular focus on the utilization of multimodal inputs and the field of generative AI. Further discussions in this review involve an approach to understanding the larger context in which AI-augmented care may exist, and include a discussion of the need for rigorous evaluation, appropriate infrastructure for deployment, ethics and equity assessments, regulatory oversight, and viable business cases for deployment. Embracing this rapidly evolving technology while setting an appropriately high evaluation benchmark with careful and patient-centered implementation will be crucial for cardiology to leverage AI to enhance patient care and the provider experience.

Keywords: artificial intelligence; cardiac imaging; clinical trials; deep learning; digital health; generative AI; health equity; implementation science; innovation; large language models; machine learning.

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Conflict of interest statement

Funding Support and Author Disclosures Dr Jain has consulting relationships with Bristol Myers Squibb, ARTIS Ventures, and Broadview Ventures. Dr Elias has research support provided to his institution from Eidos Therapeutics, Pfizer, Janssen, Edwards Lifesciences, New York Academy of Medicine, and Google. Dr Poterucha owns stock in Abbott Laboratories and Baxter International; and research support is provided to his institution from the Amyloidosis Foundation, American Heart Association (Award #933452 and #23SCISA1077494), Eidos Therapeutics, Pfizer, Janssen, Edwards Lifesciences, and the Glorney-Raisbeck Fellowship Award from the New York Academy of Medicine. Dr Avram is a co-inventor in the patent 63/208,406 (Method and System for Automated Analysis of Coronary Angiograms); and has received speaker fees from Abbott, Boston Scientific, Boehringer Ingelheim, and Novartis. Dr Avari Silva is the co-founder and consultant to and holds equity in SentiAR; the technology has been licensed by Washington University to SentiAR. Dr Maddox has received grant funding from the National Institutes of Health (NHLBI UG3HL165065: The Rhythm Evaluation for Anticoagulation with Continuous Monitoring of Atrial Fibrillation Trial [REACT-AF]); has received honoraria and/or expense reimbursement in the past 3 years from the University of Chicago, George Washington University, Baylor College of Medicine, the New York Cardiological Society, and Medscape (Dec 2022); has received compensation and travel expense reimbursement for American College of Cardiology leadership roles and meetings; is currently employed as a cardiologist and Vice President, Digital Products and Innovation at BJC HealthCare/Washington University School of Medicine, and in this capacity, he is advising Myia Labs, for which his employer is receiving equity compensation in the company, he is receiving no individual compensation from the company, and he is a compensated director for a New Mexico-based foundation, the J.F Maddox Foundation. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Figures

Figure 1.
Figure 1.. AI Product Lifecycle.
Framework for the development and deployment of clinically-relevant AI algorithms, including model generation, regulatory evaluation and quality control, and operationalization. CONSORT-AI and SPIRIT-AI are reporting guidelines for protocols of and completed randomized control trials involving AI., Per the Food and Drug Administration, anticipated modifications to the intended use, performance, or inputs of an AI software as medical device should be stated. The algorithm change protocol is a plan for how to ensure success and appropriately control the risks of the anticipated modifications, with the goal of the AI algorithm remaining safe and effective after any modifications. AI denotes Artificial Intelligence; CMS Centers for Medicare and Medicaid Services; FDA Food and Drug Administration.
Central Illustration.
Central Illustration.. Advancing Cardiovascular Care with AI.
Depiction of current inputs for AI in cardiology, and pillars for success in advancing cardiovascular care with AI.
See this image and copyright information in PMC

References

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