Year: 2025
Type: article
Study design: Observational Study
Abstract: Background: Artificial intelligence (AI) is increasingly integrated into diagnostic cardiology, offering significant potential to enhance accuracy, reduce errors, and support clinical decision-making. Despite this growth, little is known about cardiology residents’ preparedness to engage with these tools in clinical settings. Objective: To evaluate cardiology residents’ awareness, confidence, and acceptance of AI-assisted diagnostic tools in a tertiary care hospital setting. Methods: A cross-sectional study was conducted over eight months at a tertiary care hospital in Lahore. A total of 216 cardiology residents participated through purposive sampling. A structured, self-administered questionnaire was used to assess awareness, confidence, and acceptance of AI in diagnostics. Descriptive statistics and parametric tests (ANOVA, t-tests, Pearson’s correlation) were applied using SPSS version 26 to analyze normally distributed data. Ethical approval was obtained Institutional Review Board (IRB). Results: Among 216 respondents (mean age 28.3 ± 2.4 years; 57.4% male), 72.2% were familiar with AI in cardiology, while 60.6% reported knowledge of real-world applications. Confidence in interpreting AI diagnostics was limited, with only 15.7% strongly agreeing they felt comfortable. However, 74.1% accepted AI as a valuable addition to clinical practice. Significant positive correlations were observed between awareness and confidence (r = 0.62), awareness and acceptance (r = 0.55), and confidence and acceptance (r = 0.68), all with p < 0.001. Conclusion: While cardiology residents show favorable attitudes toward AI, their limited confidence and practical exposure underscore the need for structured AI training in postgraduate curricula to facilitate responsible integration into future clinical practice.
DOI: 10.71000/zsj5sb85
Authors: Areeba Sohail, Muhammad Tahir, Sadia Hameed, Fatima Tu Zohra, Iftikhar Ud Din, Tahira Kanwal
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W4411675829