A survey of 407 healthcare workers in Saudi Arabia revealed that only 42.8% are willing to use AI clinically for cardiac diagnosis, while 49.1% acknowledge lacking sufficient AI knowledge. Despite moderate acceptance levels, willingness to learn emerged as the strongest predictor of AI adoption, increasing acceptance likelihood by 3.24-fold. The study identified critical barriers: 86.7% had never used AI clinically, and less than half understood AI applications in cardiology. This reflects a broader challenge facing medical education as AI tools rapidly advance in diagnostic capabilities. The findings suggest that systematic integration of AI literacy into medical curricula could dramatically improve adoption rates, potentially accelerating the deployment of AI-assisted cardiac diagnostics that have shown promise in detecting arrhythmias, heart failure, and coronary disease earlier than traditional methods. However, this preprint awaits peer review, and results may change. The research is observational and limited to one geographic region with predominantly young, female residents. While the findings appear incremental rather than paradigm-shifting, they provide actionable insights for medical educators seeking to bridge the AI knowledge gap in cardiovascular care.