BACKGROUND: We aimed to evaluate the feasibility of implementing Chester, a novel web-based chest X-ray (CXR) interpretation artificial intelligence (AI) tool, in the medical education curriculum and explore its effect on the diagnostic performance of undergraduate medical students. METHODS: Third-year trainees were randomized in experimental (N=16) and control (N=16) groups and stratified for age, gender, confidence in CXR interpretation, and prior experience. Participants filled a

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... tion survey, a test exam (Exam1), a final exam (Exam2), and a post-intervention survey. The experimental group was allowed to use Chester during Exam1 while the control group could not. All participants were forbidden from using any resource during Exam2. The diagnostic interpretation of a fellowship-trained chest radiologist was used as standard of reference. Chester's performance itself on Exam1 was 60%. A five-point Likert scale was used to assess students' perceived confidence before/after the exams as well as Chester's perceived usefulness. RESULTS: Using a mixed model for repeated measures (MMRM), it was found that Chester did not have a statistically significant impact on the experimental group's diagnostic performance nor confidence level when compared to the control group. The experimental group rated Chester's usefulness at 3.7/5, its convenience at 4.25/5, and their likelihood to reuse it at 4.1/5. CONCLUSION: Our experience highlights the interest of medical students in using AI tools as educational resources. While the results of the pilot project are inconclusive for now, they demonstrate proof of concept for a repeat experiment with a larger sample and establish a robust methodology to evaluate AI tools in radiological education. Finally, we believe that additional research should be focused on the applications of AI in medical education so students understand this new technology for themselves and given the growing trend of remote learning.