Currently submitted to: JMIR Cardio
Date Submitted: Jul 12, 2026
Open Peer Review Period: Jul 17, 2026 - Sep 11, 2026
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Artificial intelligence preparedness in cardiovascular training: A global systematic review of educational gaps, implementation barriers, and learner readiness
ABSTRACT
Background:
This systematic review focuses on the existing literature examining awareness, attitudes, knowledge, preparedness, and educational needs related to AI among healthcare trainees and early-career professionals, within the context of cardiovascular medicine. By evaluating previous studies that have explored the gap between technological advancement and workforce readiness, this review aims to identify existing barriers, knowledge deficits, and opportunities for curriculum development.
Objective:
To synthesize the global evidence on artificial intelligence (AI) preparedness, educational gaps, and implementation barriers relevant to cardiovascular training and identify priorities for future curriculum development.
Methods:
A systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO (CRD420261443547). PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, and ERIC were searched for English-language studies published between 2018 and 2026 evaluating artificial intelligence (AI) preparedness, educational gaps, and implementation barriers among cardiology trainees and healthcare professionals. Two independent reviewers screened studies, extracted data, and resolved disagreements by consensus. Extracted outcomes included AI knowledge, confidence and preparedness, clinical exposure, educational needs, formal curriculum availability, and barriers to AI implementation in cardiovascular training.
Results:
Results:
Only a small number of studies focused exclusively on cardiology-specific populations, including cardiology residents, board-certified cardiologists, clinicians working in cardiology departments, and cardiology fellows. These studies similarly reported enthusiasm regarding AI integration into cardiovascular care, but highlighted significant concerns regarding inadequate educational preparation, limited hands-on experience, ethical considerations, and uncertainty regarding future clinical applications.
Conclusions:
This review demonstrates that the gap between enthusiasm for AI and implementation readiness is a consistent global finding. Strengthening cardiology-specific AI education through evidence-based curricula and targeted educational research will be essential to prepare future cardiologists for AI-enabled clinical practice.
Citation
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