%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e63786
%T Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study
%A Yang,Xiongwen
%A Xiao,Yi
%A Liu,Di
%A Shi,Huiyou
%A Deng,Huiyin
%A Huang,Jian
%A Zhang,Yun
%A Liu,Dan
%A Liang,Maoli
%A Jin,Xing
%A Sun,Yongpan
%A Yao,Jing
%A Zhou,XiaoJiang
%A Guo,Wankai
%A He,Yang
%A Tang,Weijuan
%A Xu,Chuan
%+ Department of Thoracic Surgery, Guizhou Provincial People's Hospital, 83 Zhongshan East Road, Guiyang, 550000, China, 86 15673740612, yangxiongwen@gz5055.com
%K radiology reports
%K doctor-patient communication
%K large language models
%K oncology
%K GPT-4
%D 2025
%7 17.4.2025
%9 Original Paper
%J J Med Internet Res
%G English
%X Background: Effective physician-patient communication is essential in clinical practice, especially in oncology, where radiology reports play a crucial role. These reports are often filled with technical jargon, making them challenging for patients to understand and affecting their engagement and decision-making. Large language models, such as GPT-4, offer a novel approach to simplifying these reports and potentially enhancing communication and patient outcomes. Objective: We aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication. Methods: In a retrospective study approved by the ethics review committees of multiple hospitals, 698 radiology reports for malignant tumors produced between October 2023 and December 2023 were analyzed. In total, 70 (10%) reports were selected to develop templates and scoring scales for GPT-4 to create simplified interpretative radiology reports (IRRs). Radiologists checked the consistency between the original radiology reports and the IRRs, while volunteer family members of patients, all of whom had at least a junior high school education and no medical background, assessed readability. Doctors evaluated communication efficiency through simulated consultations. Results: Transforming original radiology reports into IRRs resulted in clearer reports, with word count increasing from 818.74 to 1025.82 (P<.001), volunteers’ reading time decreasing from 674.86 seconds to 589.92 seconds (P<.001), and reading rate increasing from 72.15 words per minute to 104.70 words per minute (P<.001). Physician-patient communication time significantly decreased, from 1116.11 seconds to 745.30 seconds (P<.001), and patient comprehension scores improved from 5.51 to 7.83 (P<.001). Conclusions: This study demonstrates the significant potential of large language models, specifically GPT-4, to facilitate medical communication by simplifying oncological radiology reports. Simplified reports enhance patient understanding and the efficiency of doctor-patient interactions, suggesting a valuable application of artificial intelligence in clinical practice to improve patient outcomes and health care communication. 
%R 10.2196/63786
%U https://www.jmir.org/2025/1/e63786
%U https://doi.org/10.2196/63786