The large language model called ChatGPT has drawn extensively attention because of its human-like expression and reasoning abilities. In this study, we investigate the feasibility of using ChatGPT in experiments on using ChatGPT to translate radiology reports into plain language for patients and healthcare providers so that they are educated for improved healthcare. Radiology reports from 62 low-dose chest CT lung cancer screening scans and 76 brain MRI metastases screening scans were collected in the first half of February for this study. According to the evaluation by radiologists, ChatGPT can successfully translate radiology reports into plain language with an average score of 4.1 in the five-point system with 0.07 places of information missing and 0.11 places of misinformation. In terms of the suggestions provided by ChatGPT, they are general relevant such as keeping following-up with doctors and closely monitoring any symptoms, and for about 37% of 138 cases in total ChatGPT offers specific suggestions based on findings in the report. ChatGPT also presents some randomness in its responses with occasionally over-simplified or neglected information, which can be mitigated using a more detailed prompt. Furthermore, ChatGPT results are compared with a newly released large model GPT-4, showing that GPT-4 can significantly improve the quality of translated reports. Our results show that it is feasible to utilize large language models in clinical education, and further efforts are needed to address limitations and maximize their potential.

翻译：名为ChatGPT的大型语言模型因其类人表达和推理能力而受到广泛关注。本研究探讨了在实验中利用ChatGPT将放射学报告转化为患者和医疗提供者可理解的通俗语言的可行性，旨在提升其健康知识水平以改善医疗效果。研究收集了2月上半月的62例低剂量胸部CT肺癌筛查报告和76例脑MRI转移瘤筛查报告。根据放射科医师评估，ChatGPT能成功将放射学报告转化为通俗语言，在五分制评分中平均得分为4.1分，平均每份报告缺失0.07处信息、错误0.11处信息。ChatGPT提供的建议普遍具有相关性，例如建议持续随访医生并密切监测症状；在全部138例病例中，约37%的案例中ChatGPT能基于报告发现提供具体建议。ChatGPT的响应存在一定随机性，偶尔出现过度简化或信息遗漏现象，可通过更详细的提示词进行缓解。此外，将ChatGPT结果与新发布的大型模型GPT-4进行对比，显示GPT-4能显著提升翻译报告质量。研究结果表明，在临床教育中应用大型语言模型具备可行性，但需进一步努力克服局限性并最大化其潜力。