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Congress: ECR25
Poster Number: C-28535
Type: Poster: EPOS Radiologist (scientific)
Authorblock: S. Jankowski1, D. C. Rotzinger1, F. Poncet1, M. Nowak1, M. Gulizia1, J. Richiardi1, F. Ria2, C. Mourad3, C. Pozzessere1; 1Lausanne/CH, 2Durham, NC/US, 3Beyrouth/LB
Disclosures:
Sofyan Jankowski: Nothing to disclose
David Christian Rotzinger: Nothing to disclose
Florian Poncet: Nothing to disclose
Marie Nowak: Nothing to disclose
Marianna Gulizia: Nothing to disclose
Jonas Richiardi: Nothing to disclose
Francesco Ria: Nothing to disclose
Charbel Mourad: Nothing to disclose
Chiara Pozzessere: Nothing to disclose
Keywords: Artificial Intelligence, Catheter arteriography, CT, MR, Diagnostic procedure, Radiation safety, Technology assessment, Patterns of Care
Purpose While the public acknowledges the benefits of medical imaging, their understanding of the associated risks remains limited(1–3). As a result, patients are turning to the internet for answers. The rise of easily accessible artificial intelligence (AI) has transformed the way the public engages with online information, especially through language models like OpenAI’s ChatGPT.We recently published a comparative study of ChatGPT and radiology institutional websites in providing radiation protection information(4). To our knowledge, this study represented the first attempt to gauge...
Read more Methods and materials For the current study, a multidisciplinary, multicenter team of radiation protection experts, including radiologists, medical physicists, and radiographers from Switzerland (Centre Hospitalier Universitaire Vaudois, CHUV), the United States (Duke University Medical Center), and Lebanon (Centre Hospitalier Universitaire Libanais Geitaoui) retrieved five of the twelve queries tested on experts in the previous study(4) among those with the lowest variance. A questionnaire was then developed, starting with demographic questions, one of the five selected radiation protection questions, and responses generated by both...
Read more Results Quantitative analysis:To date, 109 questionnaires from Switzerland were analysed. The sample comprised 56% female participants. The age distribution was relatively homogeneous, with approximately 30% of participants in each of the following age groups: 18-39 years, 40-59 years, and 60-79 years. In terms of education, 44% of participants had attended university. Fifty five percent of the participants reported having undergone more than 10 radiological exams in their lifetime. About familiarity with chatbots, 55% of participants either did not know what a...
Read more Conclusion Unlike experts, who rated ChatGPT comparably to official institutional websites in terms of scientific adequacy, public comprehension, and overall satisfaction, patients preferred the AI-generated texts for understandability, trust, reassurance, and satisfaction. Interestingly, patients were unable to distinguish between ChatGPT and human responses. These findings suggest that while the radiation protection information provided by institutional websites is generally well-received by experts, it remains imperfect for patients. Conversely, ChatGPT appears more suitable for providing radiation protection information to the public and may...
Read more References Busey JM, Soine LA, Yager JR, Choi E, Shuman WP. Patient Knowledge and Understanding of Radiation From Diagnostic Imaging. JAMA Internal Medicine. 2013;173(3):239–241. doi: 10.1001/2013.jamainternmed.1013. Ria F, Bergantin A, Vai A, et al. Awareness of medical radiation exposure among patients: A patient survey as a first step for effective communication of ionizing radiation risks. Physica Medica: European Journal of Medical Physics. Elsevier; 2017;43:57–62. doi: 10.1016/j.ejmp.2017.10.014. Bastiani L, Paolicchi F, Faggioni L, et al. Patient Perceptions and Knowledge of Ionizing Radiation From Medical...
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