评估大型语言模型对自由文本医学生临床笔记评分的能力：定量研究。
Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study.
机构信息
Uniformed Services University of the Health Sciences, Bethesda, MD, 20814, United States, 1 301-938-2212.
Defense Health Agency, Falls Church, VA, United States.
出版信息
JMIR Med Educ. 2024 Jul 25;10:e56342. doi: 10.2196/56342.
BACKGROUND
Teaching medical students the skills required to acquire, interpret, apply, and communicate clinical information is an integral part of medical education. A crucial aspect of this process involves providing students with feedback regarding the quality of their free-text clinical notes.
OBJECTIVE
The goal of this study was to assess the ability of ChatGPT 3.5, a large language model, to score medical students' free-text history and physical notes.
METHODS
This is a single-institution, retrospective study. Standardized patients learned a prespecified clinical case and, acting as the patient, interacted with medical students. Each student wrote a free-text history and physical note of their interaction. The students' notes were scored independently by the standardized patients and ChatGPT using a prespecified scoring rubric that consisted of 85 case elements. The measure of accuracy was percent correct.
RESULTS
The study population consisted of 168 first-year medical students. There was a total of 14,280 scores. The ChatGPT incorrect scoring rate was 1.0%, and the standardized patient incorrect scoring rate was 7.2%. The ChatGPT error rate was 86%, lower than the standardized patient error rate. The ChatGPT mean incorrect scoring rate of 12 (SD 11) was significantly lower than the standardized patient mean incorrect scoring rate of 85 (SD 74; P=.002).
CONCLUSIONS
ChatGPT demonstrated a significantly lower error rate compared to standardized patients. This is the first study to assess the ability of a generative pretrained transformer (GPT) program to score medical students' standardized patient-based free-text clinical notes. It is expected that, in the near future, large language models will provide real-time feedback to practicing physicians regarding their free-text notes. GPT artificial intelligence programs represent an important advance in medical education and medical practice.
背景
教授医学生获取、解释、应用和交流临床信息所需的技能是医学教育的一个组成部分。这一过程的一个关键方面涉及为学生提供有关其自由文本临床笔记质量的反馈。
目的
本研究旨在评估大型语言模型 ChatGPT 3.5 对医学生自由文本病史和体检记录进行评分的能力。
方法
这是一项单机构、回顾性研究。标准化患者学习了一个预先指定的临床病例，并以患者身份与医学生互动。每位学生都撰写了与他们互动的自由文本病史和体检记录。学生的笔记由标准化患者和 ChatGPT 独立使用预先指定的评分量表进行评分，该量表由 85 个病例要素组成。准确性的衡量标准是正确百分比。
结果
研究人群包括 168 名一年级医学生。共有 14,280 个分数。ChatGPT 的错误评分率为 1.0%，标准化患者的错误评分率为 7.2%。ChatGPT 的错误率为 86%，低于标准化患者的错误率。ChatGPT 的平均错误评分率为 12（标准差 11），明显低于标准化患者的平均错误评分率 85（标准差 74；P=.002）。
结论
ChatGPT 与标准化患者相比显示出明显更低的错误率。这是第一项评估生成式预训练转换器（GPT）程序对医学生基于标准化患者的自由文本临床笔记进行评分的能力的研究。预计在不久的将来，大型语言模型将为执业医师提供有关其自由文本笔记的实时反馈。GPT 人工智能程序代表了医学教育和医学实践的重要进步。
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