Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study

Abstract

<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 &lt; .001), comprehensibility (0.27 points; P &lt; .01), trust (0.65 points; P &lt; .001), and reassurance (0.51 points; P &lt; .001). Findings converged with qualitative written comments (r = 0.91, P &lt; .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>