论文标题
部分可观测时空混沌系统的无模型预测
ChatGPT Makes Medicine Easy to Swallow: An Exploratory Case Study on Simplified Radiology Reports
论文作者
论文摘要
Chatgpt的发行是一种能够生成看起来像人类和真实性的文本的语言模型，在研究界超越了研究社区。我们预计，令人信服的ChatGPT表现会激发用户将其应用于各种下游任务，包括提示该模型简化自己的医疗报告。为了调查这一现象，我们进行了探索性案例研究。在问卷中，我们要求15位放射科医生评估Chatgpt简化的放射学报告的质量。大多数放射科医生都同意，简化的报告实际上是正确的，完整的，并且对患者没有可能有害。然而，报告了错误的陈述，错过的关键医疗发现和潜在有害通行证的实例。尽管需要进一步的研究，但本研究的最初见解表明，使用诸如CHATGPT之类的大型语言模型来改善放射学和其他医疗领域的患者护理。
The release of ChatGPT, a language model capable of generating text that appears human-like and authentic, has gained significant attention beyond the research community. We expect that the convincing performance of ChatGPT incentivizes users to apply it to a variety of downstream tasks, including prompting the model to simplify their own medical reports. To investigate this phenomenon, we conducted an exploratory case study. In a questionnaire, we asked 15 radiologists to assess the quality of radiology reports simplified by ChatGPT. Most radiologists agreed that the simplified reports were factually correct, complete, and not potentially harmful to the patient. Nevertheless, instances of incorrect statements, missed key medical findings, and potentially harmful passages were reported. While further studies are needed, the initial insights of this study indicate a great potential in using large language models like ChatGPT to improve patient-centered care in radiology and other medical domains.
