Medical Students’ Perspectives on Artificial Intelligence in Radiology: The Current Understanding and Impact on Radiology as a Future Specialty Choice
Author:
- 1 July 2023
- journal article
- research article
- Published by Bentham Science Publishers Ltd. in Current Medical Imaging
- Vol. 19 (8) , 1
Abstract
Background: Medical students' career choices and motivations might be significantly impacted by the rapid advances in artificial intelligence (AI) and the recent hype around it. Objective: This study aimed to assess the impact of AI on medical students’ preferences for radiology as a future specialty choice. Methods: A cross-sectional study was conducted between October and December 2021 among all medical students in the three regional medical colleges in Al-Qassim Province, Saudi Arabia. Results: The survey resulted in 319 complete responses. Among the respondents, 26.96% considered radiology to be one of their top three future specialty choices. Only a minority of the respondents (23.2%) believed that radiologists would be replaced by AI during their lifetime. The misperceptions of the potential impact of AI led 22.26% of the students to be less likely to consider a career in radiology. Students with an interest in radiology were less influenced by such misperceptions (p=.01). Based on self-reported confidence measures, the basic understanding of AI was higher among students with an interest in radiology and students with prior exposure to AI (p<.05). Conclusion: The students' preferences for radiology as a future specialty choice were influenced by their misperceptions of the potential impact of AI on the discipline. Students' interest in radiology and prior exposure to AI helped them grasp AI and eliminate the hype around it.Keywords
This publication has 51 references indexed in Scilit:
- The Role of Artificial Intelligence in Diagnostic Radiology: A Survey at a Single Radiology Residency Training ProgramJournal of the American College of Radiology, 2018
- Artificial intelligence in radiologyNature Reviews Cancer, 2018
- Canadian Association of Radiologists White Paper on Artificial Intelligence in RadiologyCanadian Association of Radiologists Journal, 2018
- Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integrationnpj Digital Medicine, 2018
- Deep Learning: A Primer for RadiologistsRadioGraphics, 2017
- Machine Learning for Medical ImagingRadioGraphics, 2017
- Deep learningNature, 2015
- Gender differences in medical students’ motives and career choiceBMC Medical Education, 2012
- Long Radiology Workdays Reduce Detection and Accommodation AccuracyJournal of the American College of Radiology, 2010
- Rising Use Of Diagnostic Medical Imaging In A Large Integrated Health SystemHealth Affairs, 2008