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.
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?
No research has yet explored what information patients consider important or significant for making treatment decisions when AI is used in a clinical setting.
“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]
Previous empirical studies on patients' acceptance or trust in AI are somewhat conflicting.
“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]
The increased anxiety can potentially be attributed to the perception of currently available AI as premature technology.
“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.
Cites
Wagner, H. R., Glaser, B. G., & Strauss, A. L. (1968) · Social Forces · doi:10.2307/2575405 · Cited by 46,470 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 524 · see citing works
The use of vignettes in qualitative research
Barter, C., & Renold, E. (1999) · Bristol Research (University of Bristol) · Cited by 453 · see citing works
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 214 · see citing works
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 67 · see citing works
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 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelli- gence in radiology. Nat Rev Cancer 2018;18: 500-10.
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 · see citing works
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 · see citing works
Langer, M., & Landers, R. N. (2021) · Computers in Human Behavior · doi:10.1016/j.chb.2021.106878 · Cited by 338 · see citing works
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 255 · see citing works
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 172 · see citing works
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 · see citing works
Ongena, Y. P., Haan, M., Yakar, D., & Kwee, T. C. (2020) · European Radiology · doi:10.1007/s00330-019-06486-0 · Cited by 161 · see citing works
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 · see citing works
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 123 · see citing works
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 114 · see citing works
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 · see citing works
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 · see citing works
Yakar, D., Ongena, Y., Kwee, T. C., & Haan, M. (2021) · Value in Health · doi:10.1016/j.jval.2021.09.004 · Cited by 110 · see citing works
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 109 · see citing works
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 · see citing works
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 105 · see citing works
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 · see citing works
Wu, C., Xu, H., Bai, D., Chen, X., Gao, J., & Jiang, X. (2023) · BMJ Open · doi:10.1136/bmjopen-2022-066322 · Cited by 81 · see citing works
Antwi, W. K., Akudjedu, T. N., & Botwe, B. O. (2021) · Insights into Imaging · doi:10.1186/s13244-021-01028-z · Cited by 78 · see citing works
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 76 · see citing works
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 · see citing works
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 · see citing works
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 69 · see citing works
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 60 · see citing works
Kang, E. Y.-N., Chen, D., & Chen, Y. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107529 · Cited by 59 · see citing works
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 56 · see citing works
Dlugatch, R., Georgieva, A., & Kerasidou, A. (2023) · BMC Medical Ethics · doi:10.1186/s12910-023-00917-w · Cited by 63 · see citing works
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 · see citing works
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 53 · see citing works
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 · see citing works
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 · see citing works
Healthcare Voice AI Assistants: Factors Influencing Trust and Intention to Use
Zhan, X., Abdi, N., Seymour, W., & Such, J. (2024) · Proc. ACM Hum. Comput. Interact. · doi:10.1145/3637339 · Cited by 65 · see citing works
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 47 · see citing works
Johansson, J. V., Dembrower, K., Strand, F., & Grauman, Å. (2024) · BMJ Open · doi:10.1136/bmjopen-2024-084014 · Cited by 45 · see citing works
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 · 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
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 · see citing works
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 · see citing works
Klonek, F. E., & Parker, S. K. (2025) · Journal of Organizational Behavior · doi:10.1002/job.70000 · Cited by 36 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 27 · see citing works
“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 · see citing works
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 · see citing works
Ozcan, B. B., Doğan, B. E., Xi, Y., & Knippa, E. E. (2025) · Radiology Imaging Cancer · doi:10.1148/rycan.240290 · Cited by 24 · see citing works
Lysø, E. H., Hesjedal, M. B., Skolbekken, J., & Solbjør, M. (2024) · Social Science & Medicine · doi:10.1016/j.socscimed.2024.116771 · Cited by 24 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Winter, P., & Carusi, A. (2022) · Journal of Responsible Technology · doi:10.1016/j.jrt.2022.100052 · Cited by 17 · 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
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 16 · see citing works
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 16 · see citing works
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 15 · see citing works
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 14 · see citing works
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 · see citing works
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 · see citing works
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 14 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 9 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Shi, H., Huang, J., Jin, Y., & Han, M. (2026) · Journal of Medical Internet Research · doi:10.2196/80581 · Cited by 9 · see citing works
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 · see citing works
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 · see citing works
Hou, J., Zhang, Z., Cheng, X., & Wang, W. (2026) · Journal of Medical Internet Research · doi:10.2196/85663 · Cited by 8 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 7 · see citing works
Stephens, J. H., Northcott, C., Poirier, B., & Lewis, T. W. (2025) · Digital Health · doi:10.1177/20552076241288631 · Cited by 7 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
Gonzalez, X. T., Steger-May, K., & Abraham, J. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae257 · Cited by 4 · see citing works
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 · see citing works
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 4 · see citing works
Kauffmann, A. L., Hennrich, J., Buck, C., & Eymann, T. (2022) · Springer eBooks · doi:10.1007/978-3-658-33597-7_29 · Cited by 4 · see citing works
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 · see citing works
Marr, C., & Tsang, Y. M. (2025) · Technical Innovations & Patient Support in Radiation Oncology · doi:10.1016/j.tipsro.2025.100300 · Cited by 3 · see citing works
Frost, E. K., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2025) · Health Expectations · doi:10.1111/hex.70185 · Cited by 3 · see citing works
The Use and Challenges of Artificial Intelligence in OtolaryngologyPreprint
Devabalan, Y. (2020) · doi:10.22541/au.159050272.21208091 · Cited by 3 · see citing works
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 · see citing works
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 · see citing works
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 · see citing works
- 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
