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Purpose: To assess whether an artificial intelligence (AI) translation of a magnetic resonance imaging (MRI) report improved patient understanding of the information presented in the radiology report and to evaluate patient preferences for AI translations over traditional radiology reports.
Methods: Patients presenting to an orthopaedic surgery clinic were prospectively enrolled and randomized into 2 groups. The standard MRI group received a traditional MRI report on a multiligament knee injury written by a radiologist, whereas the AI group received an AI-translated version of the same report, generated using ChatGPT version 4. All patients completed a standardized quiz to assess comprehension of their respective reports. After the quiz, participants were provided with both reports and asked to rate their preferences between the two. Demographic information including age, sex, race, education level, area deprivation index, and orthopaedic history was collected from all patients.
Results: A total of 64 patients (32 in each group) with an average age of 51.9 ± 15.5 years were enrolled and randomized. No significant differences in demographic characteristics were identified between the 2 groups. Patients in the AI group scored 20% higher than those in the standard MRI group on the comprehension quiz (74.7% vs 54.7%, P < .001). Overall, 87.5% of patients preferred the AI translation whereas 4.7% preferred the standard version. Patients rated the AI translation as significantly clearer than the standard version (4.5 of 5 vs 2.2 of 5, P < .001), although they had less trust in the AI translation compared with the standard report (4.1 of 5 vs 4.5 of 5, P = .003). A higher education level was predictive of comprehension.
Conclusions: AI-translated MRI reports significantly improved patient comprehension and were preferred for their clarity, despite lower trust levels compared with standard radiology reports.
Clinical Relevance: AI-translated MRI reports have the potential to enhance patient understanding of radiologic findings in orthopaedic care. However, given the low level of trust in AI-generated content observed in this study, physician oversight remains essential to ensure accuracy and sustain patient confidence.
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http://dx.doi.org/10.1016/j.arthro.2025.04.033 | DOI Listing |
EBioMedicine
September 2025
Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China; Big Data and Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, 264000, PR China. Electronic address:
J Particip Med
September 2025
Participatory Health, 20 Grasmere Ave, Fairfield, CT, 06824, United States, 1 (212) 280-1600.
JMIR Res Protoc
September 2025
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
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View Article and Find Full Text PDFJMIR Cancer
September 2025
iCARE Secure Data Environment & Digital Collaboration Space, NIHR Imperial Biomedical Research Centre, London, United Kingdom.
Background: Electronic health records (EHRs) are a cornerstone of modern health care delivery, but their current configuration often fragments information across systems, impeding timely and effective clinical decision-making. In gynecological oncology, where care involves complex, multidisciplinary coordination, these limitations can significantly impact the quality and efficiency of patient management. Few studies have examined how EHR systems support clinical decision-making from the perspective of end users.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
School of Advertising, Marketing and Public Relations, Faculty of Business and Law, Queensland University of Technology, Brisbane, Australia.
Background: Labor shortages in health care pose significant challenges to sustaining high-quality care for people with intellectual disabilities. Social robots show promise in supporting both people with intellectual disabilities and their health care professionals; yet, few are fully developed and embedded in productive care environments. Implementation of such technologies is inherently complex, requiring careful examination of facilitators and barriers influencing sustained use.
View Article and Find Full Text PDF