Comparison of Artificial Intelligence-based learning with the traditional method in the diagnosis of COVID-19 chest radiographs among postgraduate radiology residents
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Abstract
OBJECTIVE: To compare Artificial Intelligence (AI)-based teaching with traditional approach in chest radiographs to detect COVID-19 pneumonia.
METHODS: This prospective experimental randomized controlled trial was conducted at Pakistan Institute of Medical Sciences, Islamabad from July to November 2021, following ethical approval. Forty postgraduate radiology residents were randomly assigned into Group-A (traditional teaching; n=20) or Group-B (AI-based teaching; n=20) using a lottery method. Group-A engaged in one-on-one sessions for COVID X-ray reporting, while Group-B trained in AI-deep learning methods. Pre-tests assessed baseline knowledge, and post-training assessments compared learning outcomes. Statistical analysis using SPSS v25 included Independent sample t-tests and chi square test. Following initial assessments, teaching methods were exchanged between groups for comparison.
RESULTS: Out of 40 participants 60% were males and 40% were females, with mean age of 27.45±1.7 years. Group-B showed significantly higher post-test scores (9.40±0.598) compared to Group-A (7.75 ± 1.118) (p<0.001). The average improvement in scores was significantly higher in Group B based on the change from pre-test to post-test scores (p < 0.05). Significant score improvements favored Group-B across all training years (p <0.05). Gender analysis indicated similar score gains among males but significantly higher improvements in females in Group B (4.09±1.868 vs. 2.00±1.414, p<0.05).
CONCLUSION: AI approach proves significantly more time and cost efficient compared to traditional teaching methods in enhancing the ability of radiology residents. This highlight the potential of AI to optimize medical education by integration of AI technologies into radiology training programs, providing efficient, scalable, and effective learning experiences.
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