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
Cited by
Sorted by citations
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
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163
Ongena, Y. P., Haan, M., Yakar, D., & Kwee, T. C. (2020) · European Radiology · doi:10.1007/s00330-019-06486-0 · Cited by 161
Self-citationLennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D. (2021) · Journal of Medical Internet Research · doi:10.2196/24221 · Cited by 153
Machine learning in haematological malignancies
Radakovich, N., Nagy, M. R., & Nazha, A. (2020) · The Lancet Haematology · doi:10.1016/s2352-3026(20)30121-6 · Cited by 126
Stakeholders’ perspectives on the future of artificial intelligence in radiology: a scoping review
Yang, L., Ene, I. C., Belaghi, R. A., Koff, D., Stein, N., & Santaguida, P. L. (2021) · European Radiology · doi:10.1007/s00330-021-08214-z · Cited by 116
Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review
Moy, S., Irannejad, M., Manning, S. J., Farahani, M., Ahmed, Y., Gao, E., Prabhune, R., Lorenz, S., Mirza, R. M., & Klinger, C. A. (2024) · Journal of patient-centered research and reviews · doi:10.17294/2330-0698.2029 · Cited by 113
Artificial Intelligence in Imaging: The Radiologist’s Role
Rubin, D. L. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.05.036 · Cited by 110
Yakar, D., Ongena, Y., Kwee, T. C., & Haan, M. (2021) · Value in Health · doi:10.1016/j.jval.2021.09.004 · Cited by 119
Self-citationAmann, J., Vayena, E., Ormond, K. E., Frey, D., Madai, V. I., & Blasimme, A. (2023) · PLoS ONE · doi:10.1371/journal.pone.0279088 · Cited by 115
Artificial Intelligence in Screening Mammography: A Population Survey of Women’s Preferences
Ongena, Y. P., Yakar, D., Haan, M., & Kwee, T. C. (2021) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2020.09.042 · Cited by 105
Self-citationAntes, A. L., Burrous, S., Sisk, B. A., Schuelke, M. J., Keune, J. D., & DuBois, J. M. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01586-8 · Cited by 112
Patient perspectives on informed consent for medical AI: A web-based experiment
Park, H. J. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241247938 · Cited by 95
Wu, C., Xu, H., Bai, D., Chen, X., Gao, J., & Jiang, X. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-066322 · Cited by 88
Antwi, W. K., Akudjedu, T. N., & Botwe, B. O. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01028-z · Cited by 78
Dengel, A., Gehrlein, R., Fernes, D., Görlich, S., Maurer, J., Pham, H. H., Großmann, G., & Eisermann, N. D. G. (2023) · Informatics · doi:10.3390/informatics10040078 · Cited by 84
Patients’ perspectives on the use of artificial intelligence in dentistry: a regional survey
Ayad, N., Schwendicke, F., Krois, J., den Bosch, S. V., Bergé, S. J., Bohner, L. O. L., Hanisch, M. R., & Vinayahalingam, S. (2023) · Head & Face Medicine · doi:10.1186/s13005-023-00368-z · Cited by 73
Hanneman, K., Playford, D., Dey, D., van Assen, M., Mastrodicasa, D., Cook, T. S., Gichoya, J. W., Williamson, E. E., Rubin, G. D., & on behalf of the American Heart Association Council on Cardiovascular Radiology and Intervention; and Council on Lifelong Congenital Heart Disease and Heart Health in the Young (2024) · Circulation · doi:10.1161/cir.0000000000001202 · Cited by 72
Derevianko, A., Pizzoli, S. F. M., Pesapane, F., Rotili, A., Monzani, D., Grasso, R., Cassano, E., & Pravettoni, G. (2023) · Cancers · doi:10.3390/cancers15020470 · Cited by 71
Gundlack, J., Thiel, C., Negash, S., Buch, C., Apfelbacher, T., Denny, K., Christoph, J., Mikolajczyk, R., Unverzagt, S., & Frese, T. (2025) · Journal of Medical Internet Research · doi:10.2196/70487 · Cited by 66
Kang, E. Y.-N., Chen, D., & Chen, Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107529 · Cited by 59
Patients Perceptions of Artificial Intelligence in Diabetic Eye Screening
Yap, A., Wilkinson, B., Chen, E., Han, L., Vaghefi, E., Galloway, C., & Squirrell, D. (2022) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000525 · Cited by 59
Dlugatch, R., Georgieva, A., & Kerasidou, A. (2023) · BMC Medical Ethics · doi:10.1186/s12910-023-00917-w · Cited by 63
Thai, K., Tsiandoulas, K., Stephenson, E. A., Menna‐Dack, D., Shaul, R. Z., Anderson, J. A., Shinewald, A., Ampofo, A., & McCradden, M. D. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.10659 · Cited by 54
Patients’ Perspectives on Artificial Intelligence in Dentistry: A Controlled Study
Kosan, E., Krois, J., Wingenfeld, K., Deuter, C. E., Gaudin, R., & Schwendicke, F. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11082143 · Cited by 55
Farič, N., Hinder, S., Williams, R., Ramaesh, R., Bernabéu, M. O., van Beek, E. J. R., & Cresswell, K. M. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad191 · Cited by 53
Kuo, R. Y. L., Freethy, A., Smith, J. H., Hill, R., Joanna, C., Jerome, D., Harriss, E., Collins, P. G. S., Tutton, E., & Furniss, D. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102555 · Cited by 50
Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
Zhan, X., Abdi, N., Seymour, W., & Such, J. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3637339 · Cited by 46
Artificial intelligence in retinal image analysis: Development, advances, and challenges
Oganov, A. C., Seddon, I. A., Jabbehdari, S., Uner, O. E., Fonoudi, H., Yazdanpanah, G., Outani, O., & Arévalo, J. F. (2023) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2023.04.001 · Cited by 49
Johansson, J. V., Dembrower, K., Strand, F., & Grauman, Å. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084014 · Cited by 45
Artificial intelligence and the NHS: a qualitative exploration of the factors influencing adoption
Morrison, K. (2021) · Future Healthcare Journal · doi:10.7861/fhj.2020-0258 · Cited by 43
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
Armero, W., Gray, K. J., Fields, K. G., Cole, N. M., Bates, D. W., & Kovacheva, V. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac200 · Cited by 40
Parry, M. W., Markowitz, J. S., Nordberg, C. M., Patel, A. A., Bronson, W. H., & DelSole, E. M. (2023) · Indian Journal of Orthopaedics · doi:10.1007/s43465-023-00845-2 · Cited by 39
Klonek, F. E., & Parker, S. K. (2025) · Journal of Organizational Behavior · doi:10.1002/job.70000 · Cited by 41
Stogiannos, N., Gillan, C., Precht, H., dos Reis, C. S., Kumar, A., O’Regan, T., Ellis, V., Barnes, A., Meades, R., Pogose, M., et al. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101717 · Cited by 31
Maris, M. T., Koçar, A., Willems, D. L., Pols, J. J., Tan, H. L., Lindinger, G. L., & Bak, M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-024-01042-y · Cited by 30
The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
Macri, R., & Roberts, S. L. (2023) · Current Oncology · doi:10.3390/curroncol30020168 · Cited by 29
Patient views on the implementation of artificial intelligence in radiotherapy
Temple, S., Rowbottom, C. G., & Simpson, J. (2023) · Radiography · doi:10.1016/j.radi.2023.03.006 · Cited by 27
Hölgyesi, Á., Zrubka, Z., Gulàcsi, L., Baji, P., Haidegger, T. P., Kozlovszky, M., Weszl, M., Kovács, L., & Péntek, M. (2024) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-024-02470-x · Cited by 31
“How I would like AI used for my imaging”: children and young persons’ perspectives
Lee, L., Salami, R. K., Martin, H. M., Shantharam, L., Thomas, K., Ashworth, E., Allan, E., Yung, K.-W., Pauling, C., Leyden, D., et al. (2024) · European Radiology · doi:10.1007/s00330-024-10839-9 · Cited by 25
Hendrix, N., Lowry, K. P., Elmore, J. G., Lotter, W. E., Sorensen, A. G., Hsu, W. W. Y., Liao, G. J., Parsian, S., Kolb, S., Naeim, A., et al. (2022) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2022.06.019 · Cited by 25
Ozcan, B. B., Doğan, B. E., Xi, Y., & Knippa, E. E. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.240290 · Cited by 24
Lysø, E. H., Hesjedal, M. B., Skolbekken, J., & Solbjør, M. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116771 · Cited by 24
Catalina, Q. M., Femenia, J., Fuster‐Casanovas, A., Marin-Gomez, F. X., Escalé-Besa, A., Solé‐Casals, J., & Vidal‐Alaball, J. (2023) · Journal of Medical Internet Research · doi:10.2196/50728 · Cited by 23
Artificial Intelligence in Breast Imaging: Challenges of Integration Into Clinical Practice
Ozcan, B. B., Patel, B., Banerjee, I., & Doğan, B. E. (2023) · Journal of Breast Imaging · doi:10.1093/jbi/wbad007 · Cited by 22
Artificial intelligence in emergency medicine: a primer for the nonexpert
Smith, M. E., Zalesky, C. C., Lee, S., Gottlieb, M., Adhikari, S., Goebel, M., Wegman, M., Garg, N., & Lam, S. H. F. (2025) · JACEP Open · doi:10.1016/j.acepjo.2025.100051 · Cited by 18
Human-centered AI in healthcare: empowering patients and support persons in clinical decision-making
Sassi, Z., Eickmann, S., Roller, R., Osmanodja, B., Burchardt, A., Tretter, M., Samhammer, D., Dabrock, P., Möller, S., Budde, K., et al. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03298-9 · Cited by 17
Winter, P., & Carusi, A. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100052 · Cited by 20
Hemphill, S., Jackson, K., Bradley, S., & Bhartia, B. (2023) · Future Healthcare Journal · doi:10.7861/fhj.2022-0097 · Cited by 16
Frost, E. K., Bosward, R., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105417 · Cited by 19
Artificial Intelligence in the Era of Precision Oncological Imaging
Cellina, M. I., Cè, M., Khenkina, N., Sinichich, P., Cervelli, M., Poggi, V., Boemi, S., Ierardi, A. M., & Carrafiello, G. (2022) · Technology in Cancer Research & Treatment · doi:10.1177/15330338221141793 · Cited by 17
Exploring empathy in artificial intelligence: synthesis and paths for future research
Chaturvedi, A. (2024) · Information Discovery and Delivery · doi:10.1108/idd-03-2024-0048 · Cited by 16
Gatting, L., Ahmed, S., Meccheri, P., Newlands, R. S. N., Kehagia, A. A., & Waller, J. (2024) · BMJ Public Health · doi:10.1136/bmjph-2024-000892 · Cited by 15
Davis, V. H., Qiang, J. R., Adekoya, I., Howse, D., Seshie, A. Z., Kosowan, L., Delahunty‐Pike, A., Abaga, E., Cooney, J., Robinson, M., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/52244 · Cited by 14
Implementing artificial intelligence in breast cancer screening: Women’s preferences
Pearce, A. M., Carter, S. M., Frazer, H. M. L., Houssami, N., Macheras‐Magias, M., Webb, G., & Marinovich, M. L. (2025) · Cancer · doi:10.1002/cncr.35859 · Cited by 14
Schneider, D., Liedtke, W., Klausen, A. D., Lipprandt, M., Funer, F., Bratan, T., Heyen, N. B., Aichinger, H., & Langanke, M. (2025) · Digital Health · doi:10.1177/20552076251339522 · Cited by 15
Wahlich, C., Chandrasekaran, L., Chaudhry, U. A. R., Willis, K., Chambers, R., Bolter, L., Anderson, J., Shakespeare, R., Olvera-Barrios, A., Fajtl, J., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2024.111964 · Cited by 13
McGhee, K. N., Barrett, D. J., Safarini, O., Elkassem, A. M. A., Eddins, J. T., Smith, A. D., & Rothenberg, S. (2025) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-025-01629-w · Cited by 13
Huang, W., Li, Y., Bao, Z., Ye, J., Xia, W., Lv, Y., Lü, J., Wang, C., & Zhu, X. (2024) · Journal of Multidisciplinary Healthcare · doi:10.2147/jmdh.s451301 · Cited by 12
Huang, Y.-D., Zeng, S.-L., Lin, J., Mao, Z.-P., Zhang, Q.-L., Feng, G.-Z., & Ou, Q.-X. (2025) · Frontiers in Public Health · doi:10.3389/fpubh.2025.1654482 · Cited by 11
A Prospective Study Assessing Patient Perception of the Use Of Artificial Intelligence in Radiology
Clements, W., Thong, L., Zia, A., Moriarty, H. K., & Goh, G. S. (2022) · Asia Pacific Journal of Health Management · doi:10.24083/apjhm.v17i1.861 · Cited by 11
Sassi, Z., Eickmann, S., Roller, R., Osmanodja, B., Burchardt, A., Samhammer, D., Dabrock, P., Möller, S., Budde, K., & Herrmann, A. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-081318 · Cited by 9
Knowledge, interest and perspectives on Artificial Intelligence in Neurosurgery. A global survey
Boaro, A., Mezzalira, E., Siddi, F., Bagattini, C., Gabrovsky, N., Marchesini, N., Broekman, M. L. D., Sala, F., Gabrovsky, N., Ivanov, M., et al. (2024) · Brain and Spine · doi:10.1016/j.bas.2024.104156 · Cited by 11
Patient perceptions of artificial intelligence in dental imaging diagnostics: a multicentre survey
Tirapelli, C., Gaêta‐Araujo, H., Costa, E. D., Scarfe, W. C., Oliveira‐Santos, C., Fischer, K. M., Grosgogeat, B., Szönyi, V., Melo, P., Marrara, J. L., et al. (2025) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twaf018 · Cited by 9
Dontchos, B. N., Dodelzon, K., Bhole, S., Edmonds, C. E., Mullen, L. A., Parikh, J. R., Daly, C., Epling, J. A., Christensen, S., & Grimm, L. J. (2025) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2025.05.001 · Cited by 9
Patient Perspectives on Artificial Intelligence in Medical Imaging
Glenning, J., & Gualtieri, L. N. (2025) · Journal of Participatory Medicine · doi:10.2196/67816 · Cited by 9
Shi, H., Huang, J., Jin, Y., & Han, M. (2026) · Journal of Medical Internet Research · doi:10.2196/80581 · Cited by 9
Newlands, R. S. N., Bruhn, H., Rzewuska, M., Lip, G., Anderson, L. A., & Ramsay, C. R. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-10926-z · Cited by 9
Kuppanda, P. M., Janda, M., Soyer, H. P., & Caffery, L. J. (2025) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2025.01.013 · Cited by 8
Hou, J., Zhang, Z., Cheng, X., & Wang, W. (2026) · Journal of Medical Internet Research · doi:10.2196/85663 · Cited by 8
Willis, K., Chaudhry, U. A. R., Chandrasekaran, L., Wahlich, C., Olvera‐Barrios, A., Chambers, R., Bolter, L., Anderson, J., Barman, S. A., Fajtl, J., et al. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-075558 · Cited by 8
Bahakeem, B., Alobaidi, S. F., Alzahrani, A. S., Alhasawi, R. H., Alzahrani, A., Alqahtani, W., Alamer, L. A., Laswad, B. M. B., & Shanbari, N. A. (2023) · Cureus · doi:10.7759/cureus.37391 · Cited by 8
Hildebrand, R., Chang, D. T., Ewongwo, A., Ramchandran, K. J., & Gensheimer, M. F. (2023) · JCO Clinical Cancer Informatics · doi:10.1200/cci.23.00023 · Cited by 8
The knowledge and perception of patients in Malta towards artificial intelligence in medical imaging
Xuereb, F., & Portelli, J. L. (2024) · Journal of medical imaging and radiation sciences · doi:10.1016/j.jmir.2024.101743 · Cited by 7
Cavallucci, M., Andalò, A., Danesi, V., Gentili, N., Massa, I., Scarpi, E., Restuccia, M., Vespignani, R., Conficconi, A., Palleschi, M., et al. (2025) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106237 · Cited by 8
Stephens, J. H., Northcott, C., Poirier, B., & Lewis, T. W. (2025) · Digital Health · doi:10.1177/20552076241288631 · Cited by 7
The future of artificial intelligence in healthcare
Radakovich, N., & Nazha, A. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-820273-9.00016-6 · Cited by 7
Survey Research in Radiology: “Strongly Agree”
Shankar, P. R., & Maturen, K. E. (2019) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2019.07.011 · Cited by 6
Krisnan, L., Masrom, M. B., Yahya, Y., & Krishnan, M. (2025) · Asian Journal of Social Health and Behavior · doi:10.4103/shb.shb_92_24 · Cited by 5
Ogu, J. C., Ozcan, B. B., Doğan, B. E., Xi, Y., & Knippa, E. E. (2025) · Breast Cancer Research and Treatment · doi:10.1007/s10549-025-07870-9 · Cited by 5
Understanding patient views and acceptability of predictive software in osteoporosis identification
Manning, F., Mahmoud, A., & Meertens, R. M. (2023) · Radiography · doi:10.1016/j.radi.2023.08.011 · Cited by 5
Arora, P. C., Sandhu, K. K., Arora, A., Gupta, A., Waghmare, M. S., & Rampal, V. (2024) · Oral Radiology · doi:10.1007/s11282-024-00777-z · Cited by 4
Exploring scenarios for implementing fast quantitative MRI
van Hees, S., Schilder, M. B., Keyser, A., Sbrizzi, A., Kleinloog, J. P. D., & Boon, W. (2025) · European Journal of Radiology Open · doi:10.1016/j.ejro.2025.100658 · Cited by 4
Omori, M., Basnayake, P., Keogh, L. A., Frazer, H. M. L., Kunicki, K., & Lippey, J. (2025) · Health Expectations · doi:10.1111/hex.70415 · Cited by 4
Gonzalez, X. T., Steger-May, K., & Abraham, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae257 · Cited by 4
Patient Attitude on the Application of Artificial Intelligence in Diabetes Care
Masrom, M. B., Krisnan, L., Yahya, Y., & Kaundan, M. K. (2024) · International Conference on Artificial Intelligence and Data Sciences (AiDAS) · doi:10.1109/aidas63860.2024.10730338 · Cited by 4
Oladimeji, O. O., Imran, A.-A.-Z., Wang, X., & Unnikrishnan, S. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105601 · Cited by 5
Kauffmann, A. L., Hennrich, J., Buck, C., & Eymann, T. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_29 · Cited by 4
Patient Perceptions of the Use of Artificial Intelligence in Radiology: A Scoping Review
Udupa, S., Sharma, O., Sharma, S., & Yong-Hing, C. J. (2026) · Academic Radiology · doi:10.1016/j.acra.2026.01.001 · Cited by 3
Marr, C., & Tsang, Y. M. (2025) · Technical Innovations & Patient Support in Radiation Oncology · doi:10.1016/j.tipsro.2025.100300 · Cited by 3
Frost, E. K., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2025) · Health Expectations · doi:10.1111/hex.70185 · Cited by 3
The Use and Challenges of Artificial Intelligence in OtolaryngologyPreprint
Devabalan, Y. (2020) · doi:10.22541/au.159050272.21208091 · Cited by 3
Lewandowska, M., Street, D. J., Yim, J., Jones, S., & Viney, R. C. (2025) · Value in Health · doi:10.1016/j.jval.2025.09.013 · Cited by 2
AI and healthcare: surveying patient perspectives on artificial intelligence in neurosurgery
Shah, H., Ali, M., Kilgallon, J. L., Pema, P. M., O’Malley, G. R., Goldstein, I. M., & Patel, N. V. (2025) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2025.111731 · Cited by 2
Mahal, J., Mayer, C. J., Staatz, E., Wang, Y., Ehrenthal, J. C., Geisel, D., Lerch, S. P., Walter, S., & Ditzen, B. (2026) · PPmP - Psychotherapie · Psychosomatik · Medizinische Psychologie · doi:10.1055/a-2791-1642 · Cited by 2
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
