Healthcare workers' acceptance of artificial intelligence in cardiac diagnosis: Implications for medical education and training programs
Journal:
medRxiv
Published Date:
May 10, 2026
Abstract
Background The integration of artificial intelligence (AI) in cardiology requires healthcare worker acceptance for successful implementation. Understanding attitudes and educational needs is crucial for developing effective training programs. Methods A cross-sectional survey was conducted among 408 healthcare workers treating cardiac diseases in Riyadh, Saudi Arabia. We assessed AI acceptance, knowledge levels, and training preferences using validated scales. Statistical analyses included descriptive statistics, chi-square tests, correlation analysis, reliability testing, and multiple logistic regression. Results Of 408 participants, 407 provided complete responses. The sample comprised predominantly young (87.0% aged [≤]30), female (75.7%) medical residents (89.9%) with limited AI experience (86.7% never used AI clinically). Internal consistency was excellent (Cronbach's = 0.892). Moderate acceptance was observed: 49.9% were aware of AI applications in cardiology, 46.7% were willing to learn, and 42.8% were willing to use AI clinically. However, 49.1% acknowledged lacking sufficient AI knowledge. Logistic regression identified willingness to learn (OR = 3.24, 95% CI: 2.15-4.89) and training interest (OR = 2.87, 95% CI: 1.94-4.25) as the strongest predictors of AI acceptance. The model explained 68.4% of variance (Nagelkerke R2 = 0.684) with an AUC of 0.847. Conclusions Medical residents demonstrate moderate AI acceptance but significant knowledge gaps. Educational interventions--particularly hands-on learning and institutional training programs--are the strongest drivers of AI readiness, surpassing demographic predictors. Integrating AI literacy systematically into medical curricula is essential for successful AI adoption in cardiovascular care.
