TY - JOUR AU - Yang, Xiongwen AU - Xiao, Yi AU - Liu, Di AU - Shi, Huiyou AU - Deng, Huiyin AU - Huang, Jian AU - Zhang, Yun AU - Liu, Dan AU - Liang, Maoli AU - Jin, Xing AU - Sun, Yongpan AU - Yao, Jing AU - Zhou, XiaoJiang AU - Guo, Wankai AU - He, Yang AU - Tang, Weijuan AU - Xu, Chuan PY - 2025 DA - 2025/4/17 TI - Enhancing Physician-Patient Communication in Oncology Using GPT-4 Through Simplified Radiology Reports: Multicenter Quantitative Study JO - J Med Internet Res SP - e63786 VL - 27 KW - radiology reports KW - doctor-patient communication KW - large language models KW - oncology KW - GPT-4 AB - 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. SN - 1438-8871 UR - https://www.jmir.org/2025/1/e63786 UR - https://doi.org/10.2196/63786 DO - 10.2196/63786 ID - info:doi/10.2196/63786 ER -