Artificial intelligence in radiology: does it impact medical students preference for radiology as their future career?
Open accessAbstract
Objective: To test medical students’ perceptions of the impact of artificial intelligence (AI) on radiology and the influence of these perceptions on their choice of radiology as a lifetime career. Methods: A cross-sectional multicenter survey of medical students in Saudi Arabia was conducted in April 2019. Results: Of the 476 respondents, 34 considered radiology their first specialty choice, 26 considered it their second choice, and 65 considered it their third choice. Only 31% believed that AI would replace radiologists in their lifetime, while 44.8% believed that AI would minimize the number of radiologists needed in the future. Approximately 50% believed they had a good understanding of AI; however, when knowledge of AI was tested using five questions, on average, only 22% of the questions were answered correctly. Among the respondents who ranked radiology as their first choice, 58.8% were anxious about the uncertain impact of AI on radiology. The number of respondents who ranked radiology as one of their top three choices increased by 14 when AI was not a consideration. Radiology conferences and the opinions of radiologists had the most influence on the respondents’ preferences for radiology. Conclusion: The worry that AI might displace radiologists in the future had a negative influence on medical students’ consideration of radiology as a career. Academic radiologists are encouraged to educate their students about AI and its potential impact when students are considering radiology as a lifetime career choice. Advances in knowledge: Rapid advances of AI in radiology will certainly impact the specialty, the concern of AI impact on radiology had negative influence in our participants and investing in AI education and is highly recommended.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Even those with a good understanding of the technology may still have reservations.
“suggesting that even those with a good understanding of the technology may still have reservations, similar to Dahmash et al. (2020)'s findings.”
From Students’ voices on generative AI: perceptions, benefits, and challenges in higher education (2023) · cites this paper as Dahmash et al. (2020)
The Results report that students anxious about AI’s impact on radiology had higher self-perceived understanding of AI than those who were not anxious. But the Discussion says objective knowledge was low and contradicted students’ self-assessments, so the paper does not establish that students with genuinely good understanding had reservations. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
No citation lists for this paper
No citation lists for this paper yet — checked again daily.
References 20 · Cited by 92
Research works printed but not linked: 17
- [1]Pesapane F, Codari M, Sardanelli F Artificial intelligence in medical imaging: threat or opportunity? radiologists again at the forefront of innovation in medicine. Eur Radiol Exp 2018; 2: 35. doi: 10.1186/s41747-018-0061-6 30353365 PMC6199205 · DOI
- [2]Lee J-G, Jun S, Cho Y-W, Lee H, Kim GB, Seo JB, et al Deep learning in medical imaging: general overview. Korean J Radiol 2017; 18: 570– 84. doi: 10.3348/kjr.2017.18.4.570 28670152 PMC5447633 · DOI
- [4]European Society of radiology (ESR. what the radiologist should know about artificial intelligence–an ESR white paper. Insights Imaging 2019; 10: 44. 30949865 PMC6449411
- [5]Kohli M, Geis R, Ethics GR Ethics, artificial intelligence, and radiology. J Am Coll Radiol 2018; 15: 1317– 9. doi: 10.1016/j.jacr.2018.05.020 30017625 · DOI
- [6]Hricak H 2016 new horizons Lecture: beyond Imaging-Radiology of tomorrow. Radiology 2018;.; 286: 764– 75 2018 Mar. doi: 10.1148/radiol.2017171503 29346031 · DOI
- [7]Ahuja AS The impact of artificial intelligence in medicine on the future role of the physician. PeerJ 2019; 7: e7702. doi: 10.7717/peerj.7702 31592346 PMC6779111 · DOI · Paper page
- [8]Semelka RC, Busireddy KK, Burke LM, Ramalho M, Martí-Bonmatí L, Morana G, et al Radiologist income, receipts, and academic performance: an analysis of many nations. Acta Radiol 2016; 57: 1497– 507. doi: 10.1177/0284185116633914 26924837 · DOI
- [9]Chockley K, Emanuel E The end of radiology? three threats to the future practice of radiology. J Am Coll Radiol 2016; 13: 1415– 20. doi: 10.1016/j.jacr.2016.07.010 27652572 · DOI · Paper page
- [11]Chen JY, Heller MT How competitive is the match for radiology residency? present view and historical perspective. J Am Coll Radiol 2014; 11: 501– 6. doi: 10.1016/j.jacr.2013.11.011 24793041 · DOI
- [12]Abduljabbar AH, Alnajjar SF, Alshamrani H, Bashamakh LF, Alshehri HZ, Alqulayti WM, et al The influence of gender on the choice of radiology as a specialty among medical students in Saudi Arabia: cross-sectional study. Interact J Med Res 2020; 9: e14666. doi: 10.2196/14666 32141832 PMC7218599 · DOI
- [14]Gong B, Nugent JP, Guest W, Parker W, Chang PJ, Khosa F, et al Influence of artificial intelligence on Canadian medical students' preference for radiology specialty: ANational survey study. Acad Radiol 2019; 26: 566– 77. doi: 10.1016/j.acra.2018.10.007 30424998 · DOI · Paper page
- [17]Schier R Artificial intelligence and the practice of radiology: an alternative view. J Am Coll Radiol 2018; 15: 1004– 7. doi: 10.1016/j.jacr.2018.03.046 29759528 · DOI
- [18]Obermeyer Z, Emanuel EJ Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med 2016; 375: 1216– 9. doi: 10.1056/NEJMp1606181 27682033 PMC5070532 · DOI · Paper page
- [20]Sit C, Srinivasan R, Amlani A, Muthuswamy K, Azam A, Monzon L, et al Attitudes and perceptions of UK medical students towards artificial intelligence and radiology: a multicentre survey. Insights Imaging 2020; 11: 14. doi: 10.1186/s13244-019-0830-7 32025951 PMC7002761 · DOI · Paper page
- [21]Brandes GIG, D'Ippolito G, Azzolini AG, Meirelles G Impact of artificial intelligence on the choice of radiology as a specialty by medical students from the city of São Paulo. Radiol Bras 2020; 53.):: 167– 70 Jun;. doi: 10.1590/0100-3984.2019.0101 32587424 PMC7302904 · DOI
- [22]Collado-Mesa F, Alvarez E, Arheart K The role of artificial intelligence in diagnostic radiology: a survey at a single radiology residency training program. J Am Coll Radiol 2018; 15: 1753– 7. doi: 10.1016/j.jacr.2017.12.021 29477289 · DOI
- [23]Bin Dahmash A, Alorfi FK, Alharbi A, Aldayel A, Kamel AM, Almoaiqel M Burnout phenomenon and its predictors in radiology residents. Acad Radiol 2020; 27: 1033– 9. doi: 10.1016/j.acra.2019.09.024 31629625 · DOI
- Authors
- Bin Dahmash, A., Alabdulkareem, M., Alfutais, A., Kamel, A. M., Alkholaiwi, F., Alshehri, S., Al Zahrani, Y., & Almoaiqel, M.
- Affiliations
- College of Medicine, Imam Mohammad ibn Saud Islamic University, Riyadh, Saudi Arabia
- King Abdulaziz Medical City (SA)
- Cairo University (EG)
- Imam Mohammad ibn Saud Islamic University (SA)
- Vascular and Interventional Radiology Unit, Department of Medical Imaging, King Abdulaziz Medical City & King Abdullah Specialized Children’s Hospital, Ministry of National Guard-Health Affairs, Riyadh, Saudi Arabia
- Venue
- BJR|Open
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-11
- Volume
- 2
- Issue
- 1
- Pages
- 20200037
- ISSN
- 2513-9878 (ISSN-L)
- PMC
- PMC7748985
- OpenAlex
- W3097223611
- Cited by
- 92
- References
- 20
- Citations by year
- 2026: 8 · 2025: 16 · 2024: 15 · 2023: 21 · 2022: 10 · 2021: 21
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- BJR|Open (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiology practices and education
- Social Sciences › Gender Studies › Diversity and Career in Medicine
- Keywords
- artificial intelligence in radiology, medical students, career choice, student perceptions, AI knowledge, radiology conferences, artificial intelligence in education, Saudi Arabia
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
