Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study
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2026-07-07
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<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Background</jats:title> <jats:p>Radiology-risk communication affects multiple clinical specialties that use ionizing radiation, and many patients seek related information online. Prior expert evaluations found comparable performance between ChatGPT-generated and radiology-risk answers from official institutions, but patient perspectives have not been assessed.</jats:p> </jats:sec> <jats:sec> <jats:title>Purpose</jats:title> <jats:p>To assess patients’ perceptions of ChatGPT versus human-generated radiology-risk information.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods and Materials</jats:title> <jats:p>From December 2024 to March 2025, patients at 3 hospitals in the United States, Switzerland, and Lebanon were randomly assigned to 1 of 5 common radiology-risk questions. Participants, blinded to source, provided subjective ratings of both ChatGPT‑3.5 and human-generated institutional responses on 7-point Likert scales for satisfaction (primary outcome), comprehensibility, trust, and reassurance. Quantitative comparisons were performed with Inverse Normalizing Transformation, and free-text comments were analyzed using thematic coding.</jats:p> </jats:sec> <jats:sec> <jats:title>Results</jats:title> <jats:p>A total of 328 patients participated (34% aged 18-39 years, 33% aged 40-59 years, 31% aged 60-79 years, 3% aged ≥80 years; 188 female). ChatGPT responses were rated significantly higher than human responses for satisfaction (0.70-point advantage; P < .001), comprehensibility (0.27 points; P < .01), trust (0.65 points; P < .001), and reassurance (0.51 points; P < .001). Findings converged with qualitative written comments (r = 0.91, P < .05), in which ChatGPT attracted 2.3× more positive comments while human-generated responses received 1.7× more negative comments.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusions</jats:title> <jats:p>Unlike experts, patients preferred ChatGPT-generated responses to institutional materials for radiology risk questions. This divergence highlights the need for patient-centered communication and suggests that large language model-based styles, implemented with expert oversight, may improve the perceived clarity and trustworthiness of educational materials in medical specialties that use ionizing radiation.</jats:p> </jats:sec>
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Jankowski, Sofyan, Charbel Mourad, Marie Nowak, Jonas Richiardi, Wendy Brito Rodriguez, Florian Poncet, Marianna Gulizia, Stephanie de Labouchere, et al. (2026). Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study. Radiology Advances, 3(4). 10.1093/radadv/umag033 Retrieved from https://hdl.handle.net/10161/35548.
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Francesco Ria
Dr. Francesco Ria is a medical physicist and he serves as an Assistant Professor in the Department of Radiology. Francesco has an extensive expertise in the assessment of procedure performances in radiology. In particular, his research activities focus on the simultaneous evaluation of radiation dose and image quality in vivo in computed tomography providing a comprehensive evaluation of radiological exams. Moreover, Francesco is developing and investigating novel mathematical models that, uniquely in the radiology field, can incorporate a comprehensive and quantitative risk-to-benefit assessment of the procedures; he is continuing to apply his expertise towards the definition of new patient specific risk metrics, and in the assessment of image quality in vivo also using state-of-the-art imaging technology, such as photon counting computed tomography scanners, and machine learning reconstruction algorithms.
Dr. Ria is a member of the American Association of Physicists in Medicine (AAPM) task group 392 (Investigation and Quality Control of Automatic Exposure Control System in CT), of the AAPM task group 430 (Comprehensive quantification and dissemination of patient-model-based organ and effective dose estimations and their associated uncertainties for CT examinations), of the AAPM Medicine Public Education working group (WGATE), and of the Italian Association of Medical Physics task group Dose Monitoring in Diagnostic Imaging.
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