{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T17:07:29Z","timestamp":1788887249950,"version":"build-2803163510"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T00:00:00Z","timestamp":1788825600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100005760","name":"University of Gothenburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100005760","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Artificial intelligence (AI) is increasingly introduced into radiological practice to support image interpretation, workflow optimization, and diagnostic decision-making. However, successful clinical adoption depends not only on technical performance but also on radiologists\u2019 trust in these systems. This study investigates radiologists\u2019 perceptions of AI in clinical practice and examines how these perceptions relate to trust in AI-assisted diagnostic decision-making. We conducted a cross-sectional online survey among practicing radiologists in Sweden. The survey comprised eight thematic sections addressing AI knowledge and training, attitudes toward AI, responsibility attribution, information and transparency, evaluation and risk perception, professional development, and trust in AI system functionality. Survey responses were analyzed using descriptive statistics, and open-ended responses were analyzed inductively. Fifty-seven radiologists across a range of experience levels and clinical settings participated in the study. While most reported generally positive attitudes towards AI, routine clinical use and formal training were limited. Trust was primarily linked to diagnostic accuracy, empirical validation, transparency, responsibility, and continued human oversight, while concerns focused on data representativeness, system robustness in complex cases, over-reliance on AI, and insufficient transparency. Participants largely assigned responsibility for AI-assisted decisions to AI developers, radiologists, and healthcare organizations rather than to the AI systems themselves. Trust in clinical AI develops through the alignment of technological reliability, professional preparedness, and transparent governance structures. Barriers to trust emerge when education, accountability frameworks, and technological maturity do not sufficiently support clinicians\u2019 ability to critically engage with AI-assisted radiology tools in healthcare. Strengthening trust therefore requires clearer definitions of the roles, responsibilities, and competencies expected of radiologists working with AI in clinical practice.<\/jats:p>","DOI":"10.1007\/s10278-026-02176-8","type":"journal-article","created":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:16:40Z","timestamp":1788884200000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Radiologists\u2019 Trust in AI-Based Systems"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1170-9069","authenticated-orcid":false,"given":"Jabbar","family":"Hussain","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9258-8117","authenticated-orcid":false,"given":"Dina","family":"Koutsikouri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0616-122X","authenticated-orcid":false,"given":"Jan","family":"Canb\u00e4ck Ljungberg","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0352-5756","authenticated-orcid":false,"given":"\u00c5se","family":"Johnsson","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4004-2603","authenticated-orcid":false,"given":"Magnus","family":"B\u00e5th","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,8]]},"reference":[{"key":"2176_CR1","doi-asserted-by":"crossref","unstructured":"Alarifi, M. (2026). Radiologists\u2019 views on artificial intelligence and the future of radiology: Insights from a us national survey. British Journal of Radiology, 99(1177), 92\u2013101.","DOI":"10.1093\/bjr\/tqaf222"},{"key":"2176_CR2","doi-asserted-by":"crossref","unstructured":"Alves, N., Bosma, J. S., Venkadesh, K. V., Jacobs, C., Saghir, Z., De Rooij, M., . . . Huisman, H. (2023). Prediction variability to identify reduced ai performance in cancer diagnosis at mri and ct. Radiology, 308(3), e230275.","DOI":"10.1148\/radiol.230275"},{"key":"2176_CR3","doi-asserted-by":"crossref","unstructured":"Amin, K. S., Davis, M. A., Naderi, A. & Forman, H. P. (2025). Release of complex imaging reports to patients, do radiologists trust ai to help? Current problems in diagnostic radiology, 54(2), 147\u2013150.","DOI":"10.1067\/j.cpradiol.2024.12.008"},{"key":"2176_CR4","doi-asserted-by":"crossref","unstructured":"Asan, O., Bayrak, A. E. & Choudhury, A. (2020). Artificial intelligence and human trust in healthcare: Focus on clinicians. Journal of medical Internet research, 22(6), e15154.","DOI":"10.2196\/15154"},{"key":"2176_CR5","doi-asserted-by":"crossref","unstructured":"Bergquist, M., Rolandsson, B., Gryska, E., Laesser, M., Hoefling, N., Heckemann, R., . . . Bj\u00f6rkman-Burtscher, I. M. (2024). Trust and stakeholder perspectives on the implementation of ai tools in clinical radiology. European radiology, 34(1), 338\u2013347.","DOI":"10.1007\/s00330-023-09967-5"},{"key":"2176_CR6","doi-asserted-by":"crossref","unstructured":"C\u00e8, M., Ibba, S., Cellina, M., Tancredi, C., Fantesini, A., Fazzini, D., . . . Montanari, R. et\u00a0al. (2024). Radiologists\u2019 perceptions on ai integration: An in-depth survey study. European Journal of Radiology, 177, 111590.","DOI":"10.1016\/j.ejrad.2024.111590"},{"key":"2176_CR7","doi-asserted-by":"crossref","unstructured":"Chia, J. L. L., He, G. S., Ngiam, K. Y., Hartman, M., Ng, Q. X. & Goh, S. S. N. (2025). Harnessing artificial intelligence to enhance global breast cancer care: A scoping review of applications, outcomes, and challenges. Cancers, 17(2), 197.","DOI":"10.3390\/cancers17020197"},{"key":"2176_CR8","doi-asserted-by":"crossref","unstructured":"Chupetlovska, K., Georganta, E., Dignum, W., Yadav, S., Nguyen-Kim, T., Beets-Tan, R. & Trebeschi, S. (2025). 212p from skepticism to optimism: A human-centered analysis of radiologists\u2019 trust in ai. ESMO Real World Data and Digital Oncology, 10.","DOI":"10.1016\/j.esmorw.2025.100408"},{"key":"2176_CR9","doi-asserted-by":"crossref","unstructured":"Contaldo, M. T., Pasceri, G., Vignati, G., Bracchi, L., Triggiani, S. & Carrafiello, G. (2024). Ai in radiology: Navigating medical responsibility. Diagnostics, 14(14), 1506.","DOI":"10.3390\/diagnostics14141506"},{"key":"2176_CR10","doi-asserted-by":"crossref","unstructured":"Coppola, F., Faggioni, L., Regge, D., Giovagnoni, A., Golfieri, R., Bibbolino, C., . . . Grassi, R. (2021). Artificial intelligence: Radiologists\u2019 expectations and opinions gleaned from a nationwide online survey. La radiologia medica, 126(1), 63\u201371.","DOI":"10.1007\/s11547-020-01205-y"},{"key":"2176_CR11","doi-asserted-by":"crossref","unstructured":"Cox, J. C., Kerschbamer, R. & Neururer, D. (2016). What is trustworthiness and what drives it? Games and Economic Behavior, 98, 197\u2013218.","DOI":"10.1016\/j.geb.2016.05.008"},{"key":"2176_CR12","doi-asserted-by":"crossref","unstructured":"Elo, S. & Kyng\u00e4s, H. (2008). The qualitative content analysis process. Journal of advanced nursing, 62(1), 107\u2013115.","DOI":"10.1111\/j.1365-2648.2007.04569.x"},{"key":"2176_CR13","doi-asserted-by":"crossref","unstructured":"Eltawil, F. A., Atalla, M., Boulos, E., Amirabadi, A. & Tyrrell, P. N. (2023). Analyzing barriers and enablers for the acceptance of artificial intelligence innovations into radiology practice: A scoping review. 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Booz, C. et\u00a0al. (2025). Implementation of ai in radiology: The perspective of referring physicians. Insights into Imaging, 16(1), 1\u20138.","DOI":"10.1186\/s13244-025-02120-4"},{"key":"2176_CR18","doi-asserted-by":"crossref","unstructured":"H\u00f6gberg, C., Larsson, S. & L\u00e5ng, K. (2023). Anticipating artificial intelligence in mammography screening: Views of swedish breast radiologists. BMJ Health & Care Informatics, 30(1), e100712.","DOI":"10.1136\/bmjhci-2022-100712"},{"key":"2176_CR19","doi-asserted-by":"crossref","unstructured":"H\u00f6gberg, C., Larsson, S. & L\u00e5ng, K. (2024). Engaging with artificial intelligence in mammography screening: Swedish breast radiologists\u2019 views on trust, information and expertise. Digital Health, 10, 20552076241287958.","DOI":"10.1177\/20552076241287958"},{"key":"2176_CR20","doi-asserted-by":"crossref","unstructured":"Huisman, M., Ranschaert, E., Parker, W., Mastrodicasa, D., Koci, M., Pinto de Santos, D., . . . Bohyn, C. et\u00a0al. (2021). An international survey on ai in radiology in 1,041 radiologists and radiology residents part 1: Fear of replacement, knowledge, and attitude. European radiology, 31(9), 7058\u20137066.","DOI":"10.1007\/s00330-021-07781-5"},{"key":"2176_CR21","doi-asserted-by":"crossref","unstructured":"Johansson, J. V. & Engstr\u00f6m, E. (2024). \u2018humans think outside the pixels\u2019\u2013radiologists\u2019 perceptions of using artificial intelligence for breast cancer detection in mammography screening in a clinical setting. Health Informatics Journal, 30(3), 14604582241275020.","DOI":"10.1177\/14604582241275020"},{"key":"2176_CR22","doi-asserted-by":"crossref","unstructured":"L\u00e5ng, K., Josefsson, V., Larsson, A.-M., Larsson, S., H\u00f6gberg, C., Sartor, H., . . . Rosso, A. (2023). 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Elsevier.","DOI":"10.1016\/B978-0-443-13671-9.00001-6"},{"key":"2176_CR31","doi-asserted-by":"crossref","unstructured":"Pulumati, A., Pulumati, A., Dwarakanath, B. S., Verma, A. & Papineni, R. V. (2023). Technological advancements in cancer diagnostics: Improvements and limitations. Cancer Reports, 6(2), e1764.","DOI":"10.1002\/cnr2.1764"},{"key":"2176_CR32","doi-asserted-by":"crossref","unstructured":"Qurashi, A. A., Alanazi, R. K., Alhazmi, Y. M., Almohammadi, A. S., Alsharif, W. M. & Alshamrani, K. M. (2021). Saudi radiology personnel\u2019s perceptions of artificial intelligence implementation: A cross-sectional study. Journal of Multidisciplinary Healthcare, 3225\u20133231.","DOI":"10.2147\/JMDH.S340786"},{"key":"2176_CR33","doi-asserted-by":"crossref","unstructured":"Saeidnia, H. R., Firuzpour, F., Kozak, M. & Majd, H. S. (2025). Advancing cancer diagnosis and treatment: Integrating image analysis and ai algorithms for enhanced clinical practice. 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Insights into Imaging, 15(1), 240.","DOI":"10.1186\/s13244-024-01801-w"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02176-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-026-02176-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-026-02176-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T16:16:48Z","timestamp":1788884208000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-026-02176-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,8]]},"references-count":41,"alternative-id":["2176"],"URL":"https:\/\/doi.org\/10.1007\/s10278-026-02176-8","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,8]]},"assertion":[{"value":"2 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 July 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 September 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This is an observational study and was specifically designed to avoid capturing any data that could reveal a respondent\u2019s identity, such as names, IP addresses, or contact details. The authors declare that the work described has not involved experimentation on humans or animals. Furthermore, it is anticipated that the results will not adversely impact people\u2019s lives in any manner. Therefore, ethical approval was not required.","order":1,"name":"Ethics","label":"Ethics Approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"A digital affirmation of informed consent was obtained from all individual participants included in the study. A detailed statement and description of the procedure are included in the manuscript.","order":2,"name":"Ethics","label":"Consent to Participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not contain any identifiable data, images, or personal information from participants. Accordingly, consent to publish was not required.","order":3,"name":"Ethics","label":"Consent for Publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","label":"Competing Interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"During the preparation of this work, the author (JH) used ChatGPT-4 (OpenAI) to check grammar and spelling. The author (JH) subsequently reviewed and edited the content as needed and takes full responsibility for the publication\u2019s content.","order":5,"name":"Ethics","label":"Declaration on Generative AI","group":{"name":"EthicsHeading","label":"Declarations"}}]}}