Radiology advances · 2026
Patients prefer ChatGPT to institutional websites for questions on radiation-based imaging exams: international mixed-methods study.
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Jankowski Sofyan, Mourad Charbel, Nowak Marie, Richiardi Jonas, Brito Rodriguez Wendy, Poncet Florian, Gulizia Marianna, de Labouchere Stephanie, Harkness Emily, Rotzinger David, Ria Francesco, Pozzessere Chiara
Аннотация
BACKGROUND: 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.
PURPOSE: To assess patients' perceptions of ChatGPT versus human-generated radiology-risk information.
METHODS AND MATERIALS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
PURPOSE: To assess patients' perceptions of ChatGPT versus human-generated radiology-risk information.
METHODS AND MATERIALS: 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.
RESULTS: 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.
CONCLUSIONS: 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.