Artificial intelligence in radiology: who’s afraid of the big bad wolf?
Open accessAbstract
This Editorial comment refers to the article "Medical students' attitude towards artificial intelligence: a multicenter survey," Pinto Dos Santos D, et al Eur Radiol 2018. KEY POINTS: • Medical students are not well informed of the potential consequences of AI in radiology. • The fundamental principles of AI-as well as its application in medicine-must be taught in medical schools. • The radiologist specialty must actively reflect on how to validate, approve, and integrate AI algorithms into our clinical practices.
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?
The reviewed literature suggests a consensus on the importance of AI education in the medical curriculum but a void in actual planning and implementation of such education.
“Our results show that the literature suggests a consensus of opinions regarding the importance of AI education in medical curriculum but a void in actual planning and implementation of this.43,46,48”
From Educating Future Physicians in Artificial Intelligence (AI): An Integrative Review and Proposed Changes (2021) · cites this paper as [46]
AI will continue to improve its capabilities to perform analysis and provide intelligent actionable recommendations on most forms of data.
“AI will continue to improve its capabilities to perform analysis and provide intelligent actionable recommendations on most forms of data [5].”
From Needs, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum (2022) · cites this paper as [5]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362 · see citing works
Ting, D. S. W., Cheung, C. Y.-L., Lim, G., Tan, G. S. W., Quang, N. D., Gan, A., Hamzah, H., Garcia-Franco, R., San Yeo, I. Y., Lee, S. Y., et al. (2017) · JAMA · doi:10.1001/jama.2017.18152 · Cited by 2,402 · see citing works
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754 · see citing works
Detecting and classifying lesions in mammograms with Deep Learning
Ribli, D., Horváth, A., Unger, Z., Pollner, P., & Csabai, I. (2018) · Scientific Reports · doi:10.1038/s41598-018-22437-z · Cited by 696 · see citing works
Deep Learning—A Technology With the Potential to Transform Health Care
Hinton, G. (2018) · JAMA · doi:10.1001/jama.2018.11100 · Cited by 667 · see citing works
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325 · see citing works
Demystification of AI-driven medical image interpretation: past, present and future
Savadjiev, P., Chong, J., Dohan, A., Vakalopoulou, M., Reinhold, C., Paragios, N., & Gallix, B. (2019) · European Radiology · doi:10.1007/s00330-018-5674-x · Cited by 161 · see citing works
Self-citationCollado‐Mesa, F., Alvarez, E., & Arheart, K. L. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.021 · Cited by 136 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Grunhut, J., Wyatt, A. T., & Marques, O. (2021) · Journal of Medical Education and Curricular Development · doi:10.1177/23821205211036836 · Cited by 161 · see citing works
“43,46,48 Of the 6 articles mentioning plans for curricular change, there is no indication of published articles following up on these plans.”
BackgroundNeeds, Challenges, and Applications of Artificial Intelligence in Medical Education Curriculum
Grunhut, J., Marques, O., & Wyatt, A. T. M. (2022) · JMIR Medical Education · doi:10.2196/35587 · Cited by 166 · see citing works
Abuzaid, M. M., Elshami, W., McConnell, J., & Tekin, H. O. (2021) · Health and Technology · doi:10.1007/s12553-021-00583-1 · Cited by 42 · see citing works
Hesso, I., Kayyali, R., Dolton, D.-R., Joo, K., Zacharias, L., Charalambous, A., Lavdaniti, M., Stalika, E., Ajami, T., Acampa, W., et al. (2023) · Radiation Oncology · doi:10.1186/s13014-023-02351-z · Cited by 33 · see citing works
Hemphill, S., Jackson, K., Bradley, S., & Bhartia, B. (2023) · Future Healthcare Journal · doi:10.7861/fhj.2022-0097 · Cited by 16 · see citing works
- Authors
- Gallix, B., & Chong, J.
- Affiliations
- McGill University Health Centre (CA)
- Corresponding author
- Gallix, Benoit
- Venue
- European Radiology
- Published
- 2019-02-08
- Volume
- 29
- Issue
- 4
- Pages
- 1637-1639
- ISSN
- 0938-7994 (ISSN-L)
- 1432-1084
- OpenAlex
- W2914069220
- Cited by
- 48
- References
- 8
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 8 · 2023: 9 · 2022: 10 · 2021: 7 · 2020: 3 · 2019: 3
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- European Radiology (publishedVersion)
- 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
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in radiology, medical students' attitudes, radiology residency, clinical implementation of AI
- MeSH terms
- Artificial Intelligence, Humans, Surveys and Questionnaires, Radiography, Radiology, Students, Medical
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
