Background: Artificial intelligence (AI) is increasingly influencing medical students’ perceptions of radiology as a career. Studies from the United Kingdom, Switzerland, Jordan, and the Middle East reveal that while students acknowledge AI’s growing importance in healthcare, many fear that AI could replace radiologists or reduce job opportunities, impacting their specialty choices. These findings highlight the need for more research and education on AI’s role in radiology and its broader implications for the profession.
Methods: A cross-sectional survey was conducted at King Faisal University, Saudi Arabia, to assess 176 medical students’ knowledge and perceptions of AI in radiology, and how these perceptions influenced their specialty choices. Data were collected through an online questionnaire and analyzed using Statistical Package for the Social Sciences software.
Results: Of the 176 participants, 84.7% were in their clinical years, with an average age of 22.2 years. Only 5.1% of students listed radiology as their top specialty, increasing to 8% if AI were not a factor. A majority (72.2%) had limited knowledge of AI’s role in radiology, with most learning about AI from media and peers. Clinical-year students demonstrated a higher level of AI knowledge, although no significant correlation was found between AI knowledge and the choice of radiology as a specialty. Students expressed a preference for learning about AI through rotations and pre-clinical lectures.
Conclusion: Despite AI’s potential to revolutionize radiology by improving diagnostics and reducing errors, most students showed limited knowledge of AI in the field. Clinical-year students, with more exposure to radiology, had a better understanding of AI’s role. Concerns about AI reducing job opportunities were common, mirroring global trends. Media and peer discussions were the primary sources of AI information, but many students remained unaware of AI’s specific applications in radiology. Overall, AI had a minimal effect on students’ specialty preferences.
Key words: Artificial intelligence, radiology, medical students, specialty choice, King Faisal University, Saudi Arabia, cross-sectional analysis
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