Assessing the Ability of a Large Language Model to Score Free-Text Medical Student Clinical Notes: Quantitative Study.

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES JMIR Medical Education Pub Date : 2024-07-25 DOI:10.2196/56342
Harry B Burke, Albert Hoang, Joseph O Lopreiato, Heidi King, Paul Hemmer, Michael Montgomery, Viktoria Gagarin
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Abstract

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.

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评估大语言模型为自由文本医学生临床笔记评分的能力:定量研究。
背景:向医学生传授获取、解释、应用和交流临床信息所需的技能是医学教育不可或缺的一部分。这一过程的一个重要方面是向学生提供有关其自由文本临床笔记质量的反馈:本研究旨在评估大型语言模型 ChatGPT 3.5 为医学生的自由文本病史和体格检查笔记评分的能力:这是一项单一机构的回顾性研究。标准化病人学习了一个预先指定的临床病例,并作为病人与医科学生进行了互动。每位学生都会就他们之间的互动写一份自由文本的病史和体格检查记录。学生的笔记由标准化病人和 ChatGPT 使用预先指定的评分标准进行独立评分,评分标准包括 85 个病例要素。准确率的衡量标准是正确率:研究对象包括 168 名一年级医学生。结果:研究对象包括 168 名一年级医学生,总计获得 14280 分。ChatGPT 错误评分率为 1.0%,标准化病人错误评分率为 7.2%。ChatGPT 的错误率为 86%,低于标准化病人的错误率。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:与标准化患者相比,ChatGPT 的错误率明显较低。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病人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的自由文本笔记提供实时反馈。GPT 人工智能程序是医学教育和医学实践的重要进步。
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来源期刊
JMIR Medical Education
JMIR Medical Education Social Sciences-Education
CiteScore
6.90
自引率
5.60%
发文量
54
审稿时长
8 weeks
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