The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
A Qualitative Study to Understand Patient Perspective on the Use of Artificial Intelligence in Radiology
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Credits awarded for this enduring activity are designated “SA-CME” by the American Board of Radiology (ABR) and qualify toward fulfilling requirements for Maintenance of Certification (MOC) Part II: Lifelong Learning and Self-assessment. To access the SA-CME activity visit https://cortex.acr.org/Presenters/CaseScript/CaseView?CDId=ULjRyQ8Jbyg%3d. SA-CME credit expires April 2022.
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“Although the value of empirical research on patients’ views regarding information disclosure in the use of AI/ML in medicine has been recognized, 12 no research has yet explored what information patients consider important or significant for making treatment decisions when AI is used in a clinical setting.26–28”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [26]
In the citing paper
“The results of previous empirical studies on patients’ acceptance or trust in AI are somewhat conflicting. Some studies have shown that participants are more accepting of AI if it is proven to be more accurate than providers26,29 or if the use of AI fits societal and cultural norms.35,36 However, other studies have found that trust in AI did not increase when participants were informed that the AI”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [26]
In the citing paper
“This anxiety can potentially be attributed to the perception of currently available AI as premature technology,26,30–33 which could be addressed by providing more information about its effectiveness and exact application in relation to the AI tool.”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [26]
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Wagner, H. R., Glaser, B. G., & Strauss, A. L. (1968) · Social Forces · doi:10.2307/2575405 · Cited by 46,517
The Discovery of Grounded Theory: Strategies for Qualitative Research
Brodsky, C. (1968) · Psychosomatics · doi:10.1016/s0033-3182(68)71872-7 · Cited by 6,727
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803
Thrall, J. H., Li, X., Li, Q., Cruz, C., Do, S., Dreyer, K., & Brink, J. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.026 · Cited by 710
Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
Tang, A., Tam, R. C., Cadrin-Chênevert, A., Guest, W. C., Chong, J., Barfett, J., Chepelev, L. L., Cairns, R., Mitchell, J. R., Cicero, M. D., et al. (2018) · Canadian Association of Radiologists Journal · doi:10.1016/j.carj.2018.02.002 · Cited by 549
The use of vignettes in qualitative research
Barter, C., & Renold, E. (1999) · Bristol Research (University of Bristol) · Cited by 453
Scenarios as a scholarly methodology to produce “interesting research”
Ramírez, R., Mukherjee, M., Vezzoli, S., & Kramer, A. M. (2015) · Futures · doi:10.1016/j.futures.2015.06.006 · Cited by 243
Data Science in Radiology: A Path Forward
Aerts, H. J. W. L. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2804 · Cited by 70
Gyftopoulos, S., Smith, S. W., Simon, E., Kuznetsova, M., Horwitz, L. I., & Makarov, D. V. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.08.022 · Cited by 18
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Machine Learning and Deep Learning in Medical Imaging: Intelligent Imaging
Currie, G. M., Hawk, K. E., ROHREN, E. M., Vial, A., & Klein, R. (2019) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2019.09.005 · Cited by 454
Young, A. T., Amara, D., Bhattacharya, A., & Wei, M. L. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00132-1 · Cited by 400
Langer, M., & Landers, R. N. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106878 · Cited by 338
Fritsch, S. J., Blankenheim, A., Wahl, A., Hetfeld, P., Maaßen, O., Deffge, S., Kunze, J., Rossaint, R., Riedel, M., Marx, G., et al. (2022) · Digital Health · doi:10.1177/20552076221116772 · Cited by 272
Vo, 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 177
Metadata
- Authors
- Haan, M., Ongena, Y. P., Hommes, S., Kwee, T. C., & Yakar, D.
- Affiliations
- University of Groningen (NL)
- University Medical Center Groningen (NL)
- Corresponding author
- Haan, Marieke
- Venue
- Journal of the American College of Radiology
- Publisher
- Elsevier BV
- Published
- 2019-10
- Volume
- 16
- Issue
- 10
- Pages
- 1416-1419
- ISSN
- 1546-1440 (ISSN-L)
- OpenAlex
- W2921174746
- Cited by
- 141
- References
- 9
- Citations by year
- 2026: 15 · 2025: 29 · 2024: 44 · 2023: 20 · 2022: 13 · 2021: 12 · 2020: 4 · 2019: 4
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American College of Radiology (publishedVersion, other-oa)
- Research portal (Tilburg University) (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, other-oa)
- Funders
- Rijksuniversiteit Groningen
- 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, radiology, patient perspectives, patient attitudes, clinical implementation of AI, patient experience
- MeSH terms
- Adult, Aged, Artificial Intelligence, Attitude to Health, Female, Humans, Male, Middle Aged, Patient Acceptance of Health Care, Patients, Tomography, X-Ray Computed, Qualitative Research
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