Evaluation of knowledge and attitudes of radiology department workers about artificial intelligence
Open accessThe 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?
Many healthcare professionals would not assume accountability for results provided by AI.
“Also, it appears that many would not assume accountability for results provided by AI (6).”
From Integrating AI in medical education: embracing ethical usage and critical understanding (2023) · cites this paper as [6]
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
Artificial intelligence in healthcare: past, present and future
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017) · Stroke and Vascular Neurology · doi:10.1136/svn-2017-000101 · Cited by 4,837 · cited 1× · see citing works
“Medical imaging has been the most commonly searched area in AI, and the number of publications on AI have risen to 700-800 from 100-150 in the last decade [12, 13].”
BackgroundPredicting the future - big data, machine learning, and clinical medicine
Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652 · cited 1× · see citing works
“The majority of AI experts think that the developments of this technology will increase the effectiveness of healthcare in the medical field and diagnostic accuracy and will reduce the workload of physicians [6-8].”
BackgroundMachine Learning in Medical Imaging
Suzuki, K., Yan, P., Wang, F., et al. (2012) · Machine Vision and Applications · doi:10.1007/s00138-013-0543-8 · Cited by 925 · cited 1× · see citing works
“AI imaging interpretation will affect the reporting and communication of the results [5].”
BackgroundPesapane, F., Codari, M., & Sardanelli, F. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0061-6 · Cited by 839 · cited 3× · see citing works
“Medical imaging has been the most commonly searched area in AI, and the number of publications on AI have risen to 700-800 from 100-150 in the last decade [12, 13].”
BackgroundAutomated medical image segmentation techniques
Sharma, N., & Aggarwal, L. (2010) · Journal of Medical Physics · doi:10.4103/0971-6203.58777 · Cited by 754 · cited 1× · see citing works
“…used in the discussion of a wide range of medical imaging problems, such as imaging segmentation (detection of the limits of a targeted object) [2], registration (visual alignment of anatomic parts on single or multimodality images) [3], and the detection and classification of…”
BackgroundPhysician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387 · cited 3× · see citing works
“In a study by Blease et al. from the UK, the opinions of family physicians were evaluated, and participants reported that cAI is not likely to replace physicians in the near future [15].”
BackgroundInfluentialImplementing Machine Learning in Radiology Practice and Research
Kohli, M. D., Prevedello, L. M. S., Filice, R. W., & Geis, J. R. (2017) · American Journal of Roentgenology · doi:10.2214/ajr.16.17224 · Cited by 301 · cited 1× · see citing works
“187 Introduction Artificial intelligence (AI) is defined as computers that behave in the ways that previously thought to require human intelligence and have significant potential to considerably improve radiology, assist patients and reduce costs [1].”
BackgroundPhysician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297 · cited 1× · see citing works
“In the same study, only 17.6% of the participants reported that AI may replace themselves [16].”
BackgroundInfluentialThe Patient Will See You Now: The Future of Medicine is in Your Hands
Parish, J. (2015) · Journal of Clinical Sleep Medicine (JCSM) · doi:10.5664/jcsm.4788 · Cited by 273 · cited 1× · see citing works
“The majority of AI experts think that the developments of this technology will increase the effectiveness of healthcare in the medical field and diagnostic accuracy and will reduce the workload of physicians [6-8].”
BackgroundThe future of radiology augmented with Artificial Intelligence: A strategy for success
Liew, C. J. Y. (2018) · European Journal of Radiology · doi:10.1016/j.ejrad.2018.03.019 · Cited by 258 · cited 1× · see citing works
“In general, AI programs that are being developed include image detection, segmentation, and precise information linking genomic and imaging data [14].”
BackgroundArtificial intelligence in healthcare: a critical analysis of the legal and ethical implications
Schönberger, D. (2019) · International Journal of Law and Information Technology · doi:10.1093/ijlit/eaz004 · Cited by 230 · cited 1× · see citing works
“It is unclear who will be responsible for poor outcomes of AI in the future [11].”
BackgroundA Survey on Artificial Intelligence Approaches for Medical Image Classification
Deepa, S., & Devi, B. (2011) · doi:10.17485/ijst/2011/v4i11/30291 · Cited by 172 · cited 1×
“) is currently used in the discussion of a wide range of medical imaging problems, such as imaging segmentation (detection of the limits of a targeted object) [2], registration (visual alignment of anatomic parts on single or multimodality images) [3], and the detection and classification of formations/structures (gro…”
BackgroundGeis, J. R., Brady, A. P., Wu, C. C., Spencer, J., Ranschaert, E. R., Jaremko, J. L., Langer, S. G., Kitts, A. B., Birch, J. V., Shields, W. F., et al. (2019) · Insights into Imaging · doi:10.1186/s13244-019-0785-8 · Cited by 121 · cited 1× · see citing works
“These resources are not evenly distributed, and this may lead to denial of access to potential benefits from AI by some countries, regions and subgroups [10].”
BackgroundComputerization and the future of primary care: A survey of general practitioners in the UK
Blease, C. R., Bernstein, M. H., Gaab, J., Kaptchuk, T. J., Kossowsky, J., Mandl, K. D., Davis, R. B., & DesRoches, C. M. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207418 · Cited by 92 · cited 1× · see citing works
“In the same study, the majority of the participants (94%) reported that technology can not provide emphatic care as or better than physicians [17].”
BackgroundA Glimpse of the Next 100 Years in Medicine
Kohane, I. S., Drazen, J. M., & Campion, E. W. (2012) · New England Journal of Medicine · doi:10.1056/nejme1213371 · Cited by 74 · cited 1× · see citing works
“The majority of AI experts think that the developments of this technology will increase the effectiveness of healthcare in the medical field and diagnostic accuracy and will reduce the workload of physicians [6-8].”
BackgroundKnowledge, Attitudes and Perspectives of Anesthesiologists on Artificial Intelligence
Tasdogan, A. (2020) · Eurasian Journal of Medical Investigation · doi:10.14744/ejmi.2020.54709 · Cited by 7 · cited 2×
“Again in a survey conducted among psychiatrists in the Duke University and Harvard Medical Faculty, only 4% of the participants stated that AI may replace themselves [9].”
Background
Flagged first, then by citations
3 of the 19 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [3]3. Wyawahare MV, Patil PM, Abhyankar HK. Image registration techniques: an overview. Int J Signal Process. Image Process Pattern Recognit. 2009;2(3):11-28.
- [18]18. Hoek E, Diederichs M. Engineering and hydraulics in jointed rock masses. Int J Rock Mech Min Sci. 2006;43:203-15.
Cited by
Sorted by citations
Lambert, S. I., Madi, M., Sopka, S., Lenes, A., Stange, H., Buszello, C.-P., & Stephan, A. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00852-5 · Cited by 404 · see citing works
Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258 · see citing works
“5 years (range 19–37); 47% female AI in dermatology Yurdaisik and Aksoy (37) Quantitative Physicians, residents, and technicians working in radiology departments of various hospitals and medical students in Istinye university, in Turkey 204 81.”
BackgroundMousavi Baigi, S. F., Sarbaz, M., Ghaddaripouri, K., Ghaddaripouri, M., Mousavi, A. S., & Kimiafar, K. (2023) · Health Science Reports · doi:10.1002/hsr2.1138 · Cited by 255 · see citing works
“36,42,51 In the study by Boillat et al., medical students believed that the use of AI in medicine increased risks for patients and the medical community.”
BackgroundArtificial intelligence in medical education - perception among medical students
Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A. (2024) · BMC Medical Education · doi:10.1186/s12909-024-05760-0 · Cited by 185 · see citing works
“In fact, a paper published by Yurdasik et al. in 2021 had respondents encouraging practitioners to move away from specialisations that used AI [34].”
BackgroundVo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Nga, Q., & Woode, M. E. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2023.116357 · Cited by 172 · see citing works
Integrating AI in medical education: embracing ethical usage and critical understanding
Alam, F., Lim, M. A., & Zulkipli, I. N. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1279707 · Cited by 82 · see citing works
Anxiety about artificial intelligence from patient and doctor-physician
Li, W., & Liu, X. (2024) · Patient Education and Counseling · doi:10.1016/j.pec.2024.108619 · Cited by 42 · see citing works
Botha, N. N., Ansah, E. W., Segbedzi, C. E., Dumahasi, V. K., Maneen, S., Kodom, R. V., Tsedze, I. S., Akoto, L. A., & Atsu, F. S. (2024) · BMC Digital Health · doi:10.1186/s44247-024-00091-y · Cited by 17 · see citing works
Artificial Intelligence in Medical Education- Perception Among Medical Students
Jackson, P., S, G. P., Babu, C., Tony, C., Jack, D. S., Davis, D., R, R. V., Kurian, N., & John, A. (2024) · doi:10.21203/rs.3.rs-3833999/v1 · Cited by 187 · see citing works
El‐Kassas, M., Khalifa, R., Medhat, M. A., Yilmaz, Y. N., Tumi, A., Labidi, A., Almattooq, M., Sanai, F. M., Elbadry, M., Mohammed, M. O., et al. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1630831 · Cited by 4 · see citing works
“These concerns align with findings from the broader international literature, which frequently cites the absence of ethical and legal standards as major impediments to AI integration in healthcare (15, 44, 45).”
Durukan, N., Görücü, R., & Ayoğlu, T. (2025) · Journal of Clinical Nursing · doi:10.1111/jocn.70108 · Cited by 4 · see citing works
Chen, M., Zhang, B., Cai, Z., Seery, S., Mendez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4128867 · Cited by 2 · see citing works
Park, K., Kim, K., Kim, D., Kim, K., Seo, W.-Y., Shin, J.-M., Kim, J., & Kim, S. (2024) · doi:10.36227/techrxiv.171779115.51311145/v1
Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A. (2024) · Figshare · doi:10.6084/m9.figshare.26754622
Jackson, P., Ponath Sukumaran, G., Babu, C., Tony, M. C., Jack, D. S., Reshma, V. R., Davis, D., Kurian, N., & John, A. (2024) · Figshare · doi:10.6084/m9.figshare.26754625
- Authors
- Yurdaisik, I., & Aksoy, S. H.
- ORCID
- Yurdaisik, Isil
- Venue
- Annals of Clinical and Analytical Medicine
- Publisher
- Bayrakol Medical Publishing
- Published
- 2021-02-01
- Volume
- 12
- Issue
- 02
- Pages
- 186-190
- ISSN
- 2667-663X (ISSN-L)
- OpenAlex
- W4251738771
- Cited by
- 13
- References
- 19
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 4 · 2022: 2
- FWCI
- 0.89
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The Annals of Clinical and Analytical Medicine (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
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, radiology staff, knowledge, attitudes, artificial intelligence adoption
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
