Yang Xiongwen, Xiao Yi, Liu Di, Shi Huiyou, Deng Huiyin, Huang Jian, Zhang Yun, Liu Dan, Liang Maoli, Jin Xing, Sun Yongpan, Yao Jing, Zhou XiaoJiang, Guo Wankai, He Yang, Tang Weijuan, Xu Chuan
Department of Thoracic Surgery, Guizhou Provincial People's Hospital, Guiyang, China.
NHC Key Laboratory of Pulmonary Immunological Diseases, Guizhou Provincial People's Hospital, Guiyang, China.
J Med Internet Res. 2025 Apr 17;27:e63786. doi: 10.2196/63786.
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.
We aimed to assess the feasibility and effectiveness of using GPT-4 to simplify oncological radiology reports to improve physician-patient communication.
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.
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).
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.
有效的医患沟通在临床实践中至关重要，尤其是在肿瘤学领域，放射学报告起着关键作用。这些报告常常充斥着专业术语，患者难以理解，进而影响他们的参与度和决策。诸如GPT-4之类的大语言模型提供了一种简化这些报告的新方法，并有可能加强沟通及改善患者治疗效果。
我们旨在评估使用GPT-4简化肿瘤放射学报告以改善医患沟通的可行性和有效性。
在一项经多家医院伦理审查委员会批准的回顾性研究中，分析了2023年10月至2023年12月期间生成的698份恶性肿瘤放射学报告。总共挑选了70份（10%）报告来为GPT-4开发模板和评分量表，以创建简化的放射学解释报告（IRR）。放射科医生检查了原始放射学报告与IRR之间的一致性，而患者的志愿者家属（均至少具有初中文化水平且无医学背景）评估了可读性。医生通过模拟会诊评估沟通效率。
将原始放射学报告转化为IRR后，报告更清晰，字数从818.74增加到1025.82（P<0.001），志愿者阅读时间从674.86秒减少到589.92秒（P<0.001），阅读速度从每分钟72.15个单词提高到104.70个单词（P<0.001）。医患沟通时间显著减少，从1116.11秒降至745.30秒（P<0.001），患者理解得分从5.51提高到7.83（P<0.001）。
本研究证明了大语言模型，特别是GPT-4，通过简化肿瘤放射学报告促进医学沟通的巨大潜力。简化报告增强了患者的理解以及医患互动的效率，表明人工智能在临床实践中对改善患者治疗效果和医疗沟通具有重要应用价值。
