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
Open accessAbstract
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.
Overview
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
Verification
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?
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
Flagged first, then by citations
Cited by
Sorted by citations
Items 101–142 of 142
Messelink, M. A., Fadaei, S., Verhoef, L. M., Welsing, P. M. J., Nijhof, N., & Westland, H. (2024) · Rheumatology · doi:10.1093/rheumatology/keae202 · Cited by 2
Enhancing Patients' E-Health Engagement Through Artificial Intelligence
Chauhan, A. S., Gulati, C., Mathur, G., & Sankpal, S. (2024) · Advances in healthcare information systems and administration book series · doi:10.4018/979-8-3693-5468-1.ch002 · Cited by 1
The AI doctor will see you now: public perspectives on artificial intelligence in healthcare
Horst, C., Aniq, M., Taylor-Gee, A., Wong, J. S.-C., & Goh, V. (2025) · BJR|Artificial Intelligence · doi:10.1093/bjrai/ubaf003 · Cited by 1
Jiang, T., Wei, N., Yan, Q., & Ye, W. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1693397 · Cited by 1
Jovanović, S., Vukičević, J. P., Kilibarda, B., Milosavljević, M., & Bjegovic-Mikanovic, V. M. (2026) · Frontiers in Radiology · doi:10.3389/fradi.2026.1714029 · Cited by 1
Cha, J. (2026) · The International Journal on Media Management · doi:10.1080/14241277.2026.2667537 · Cited by 1
Artificial intelligence in dentistry: awareness among dentists and computer scientists
Costa, E. D., Vieira, M. A., Ambrosano, G. M. B., Gaêta‐Araujo, H., Carneiro, J. A., Zancan, B. A. G., Scaranti, A., Macedo, A. A., & Tirapelli, C. (2025) · Oral Radiology · doi:10.1007/s11282-025-00828-z · Cited by 1
Artificial Intelligence in Pharmacy: Transforming Practice and Research
Qureshi, M., Shaikh, A., Shaikh, S., & SHAIKH, A. (2025) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/978-94-6463-852-3_34 · Cited by 1
Schilder, M. B., Keyser, A., van Hees, S., Sbrizzi, A., & Boon, W. (2026) · Journal of Medical Internet Research · doi:10.2196/83407 · Cited by 1
Sümbüllü, M., Adam, E. O., ALTUN, E. A., & Arslan, H. (2026) · BMC Oral Health · doi:10.1186/s12903-026-08327-1 · Cited by 1
Wilson, N. A. (2022) · Clinical Orthopaedics and Related Research · doi:10.1097/corr.0000000000002463 · Cited by 1
Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to UsePreprint
Zhan, X., Abdi, N., Seymour, W., & Such, J. M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.04543 · Cited by 1
Editorial: Artificial intelligence and machine learning in pediatrics
Kuru, K., Caswell, N., Hart, G. R., Quon, J. L., & Ehwerhemuepha, L. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1814141 · Cited by 1
Artificial Intelligence in Radiology-Breast Imaging and Beyond
Young, L. K. (2022) · American Journal of Biomedical Science & Research · doi:10.34297/ajbsr.2022.15.002101
Diagnostic, prognostic, and therapeutic biomarkers of ovarian cancer
Asare-Werehene, M., Tsuyoshi, H., Lee, E. Y. P., Chiu, K. W.-H., Ngu, S., Ngan, H., Chan, K. K. L., Yoshida, Y., & Tsang, B. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-824040-3.00005-4
Stakeholder Engagement and Communication
Berigan, K., Cook, T. S., & Ranschaert, E. R. (2024) · Imaging informatics for healthcare professionals · doi:10.1007/978-3-031-68942-0_4
(2025) · Zeitschrift für Gemeinwirtschaft und Gemeinwohl · doi:10.5771/2701-4193-2025-2-105
Bridging Bites with Bytes: Redefining Periodontal Diagnosis
Tazeen, S., Das, R., Rao, P. V. D., Rekha, K. S., Neelakanti, A., Thakur, A., & Bachu Bhavya, T. (2024) · Clinical Dentistry · doi:10.33882/clinicaldent.15.35938
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26715280
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · Figshare · doi:10.6084/m9.figshare.26702278
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.25744290
Hölgyesi, Á., Zrubka, Z., Gulácsi, L., Baji, P., Haidegger, T. P., Kozlovszky, M., Weszl, M., Kovács, L., & Péntek, M. (2024) · Figshare · doi:10.6084/m9.figshare.25511856
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26715277
Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · Figshare · doi:10.6084/m9.figshare.15026439
Hölgyesi, Á., Zrubka, Z., Gulácsi, L., Baji, P., Haidegger, T. P., Kozlovszky, M., Weszl, M., Kovács, L., & Péntek, M. (2024) · Figshare · doi:10.6084/m9.figshare.25511889
Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · Figshare · doi:10.6084/m9.figshare.15026430
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · Figshare · doi:10.6084/m9.figshare.25547136
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26715283
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · Figshare · doi:10.6084/m9.figshare.25547013
Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · Figshare · doi:10.6084/m9.figshare.15026436
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · Figshare · doi:10.6084/m9.figshare.26702275
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.26715286
Hölgyesi, Á., Zrubka, Z., Gulácsi, L., Baji, P., Haidegger, T. P., Kozlovszky, M., Weszl, M., Kovács, L., & Péntek, M. (2024) · Figshare · doi:10.6084/m9.figshare.26699621
Newlands, R. S. N., Bruhn, H., Díaz, M. R., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · Figshare · doi:10.6084/m9.figshare.25744478
Antes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · Figshare · doi:10.6084/m9.figshare.15026433
Current patient attitudes to artificial intelligence applications in radiology
Maclean, R., Aniq, M. I., Delaney, J., Kang, Y., O’Shea, R., Mazumder, A., Horst, C., & Goh, V. (2026) · British Journal of Radiology · doi:10.1093/bjr/tqag077
Kuppanda, P. M., Janda, M., & Caffery, L. J. (2026) · Interactive Journal of Medical Research · doi:10.2196/92969
Gillan, C., Liszewski, B., Hsu, A., Rahman, F. H., Ebady, M., Nielsen, M. K., Aloysious, C., Lai, Y., Brown, E. M., Donaldson, H., et al. (2026) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2026.102436
Li, X. J., Jing, C., Gong, Z., Zhang, J., & Zhang, S. (2026) · Frontiers in Digital Health · doi:10.3389/fdgth.2026.1842497
Gillan, C., Liszewski, B., Hsu, A., Rahman, F. H., Ebady, M., Nielsen, M. K., Aloysious, C., Lai, Y., Brown, E. M., Donaldson, H., et al. (2026) · Journal of Applied Clinical Medical Physics · doi:10.1002/acm2.70658
Ansari, M., & Abhishek (2026) · Journal of Administrative Development · doi:10.48001/jad.202641.90-103
In Algorithms We Trust? Stakeholder Perspectives on Algorithmic and AI-Based Survival Calculators
Hurley, M. E., Kostick, K. M., Smith, J. N., Smolenski, J., Dexter, R., & Blumenthal‐Barby, J. (2026) · AJOB Empirical Bioethics · doi:10.1080/23294515.2026.2718056
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
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
