探索 ChatGPT 3.5、4 和 4 版在智利医师资格考试中的表现:观察研究。

IF 3.2 Q1 EDUCATION, SCIENTIFIC DISCIPLINES
Marcos Rojas, Marcelo Rojas, Valentina Burgess, Javier Toro-Pérez, Shima Salehi
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引用次数: 0

摘要

背景介绍OpenAI 的 ChatGPT-3.5 及其后续版本 ChatGPT-4 和 ChatGPT-4 With Vision(4V,又称 "GPT-4 Turbo With Vision")的部署对医疗领域产生了显著影响。这些模型在全球医学考试中表现出色,显示出教育应用的潜力。然而,它们在非英语环境中的有效性,尤其是在智利医疗执照考试中的有效性(这是智利医疗从业人员的关键步骤),却鲜有人问津。这一空白凸显了评估 ChatGPT 在不同语言和文化背景下适应性的必要性:本研究旨在评估 ChatGPT 3.5、4 和 4V 版本在智利主要医学考试 EUNACOM(Examen Único Nacional de Conocimientos de Medicina)中的表现:使用智利大学的三个官方练习(540 道题)测试 ChatGPT 3.5、4 和 4V 版本,这些练习与 EUNACOM 的结构和难度一致。3 个 ChatGPT 版本的每次练习都提供了 3 次尝试机会。对每次尝试中的问题回答进行了系统分类和分析,以评估其准确率:所有版本的 ChatGPT 都通过了 EUNACOM 演习。结果:所有版本的 ChatGPT 都通过了欧盟海军司令部的演习,特别是 4 和 4V 版本的表现优于 3.5 版本,平均准确率分别达到 79.32% 和 78.83%,而 3.5 版本的准确率为 57.53%(PConclusions:本研究揭示了 ChatGPT 通过 EUNACOM 考试的能力,3.5、4 和 4V 版本的熟练程度截然不同。值得注意的是,人工智能(AI)的进步并未显著提高基于图像问题的成绩。不同医学领域的熟练程度存在差异,这表明需要进行更细致的人工智能培训。此外,这项研究还强调了探索创新方法的重要性,即利用人工智能增强人类认知能力并强化学习过程。这种进步有可能对医学教育产生重大影响,不仅能促进知识的获取,还能培养医疗保健专业人员的批判性思维和解决问题的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring the Performance of ChatGPT Versions 3.5, 4, and 4 With Vision in the Chilean Medical Licensing Examination: Observational Study.

Background: The deployment of OpenAI's ChatGPT-3.5 and its subsequent versions, ChatGPT-4 and ChatGPT-4 With Vision (4V; also known as "GPT-4 Turbo With Vision"), has notably influenced the medical field. Having demonstrated remarkable performance in medical examinations globally, these models show potential for educational applications. However, their effectiveness in non-English contexts, particularly in Chile's medical licensing examinations-a critical step for medical practitioners in Chile-is less explored. This gap highlights the need to evaluate ChatGPT's adaptability to diverse linguistic and cultural contexts.

Objective: This study aims to evaluate the performance of ChatGPT versions 3.5, 4, and 4V in the EUNACOM (Examen Único Nacional de Conocimientos de Medicina), a major medical examination in Chile.

Methods: Three official practice drills (540 questions) from the University of Chile, mirroring the EUNACOM's structure and difficulty, were used to test ChatGPT versions 3.5, 4, and 4V. The 3 ChatGPT versions were provided 3 attempts for each drill. Responses to questions during each attempt were systematically categorized and analyzed to assess their accuracy rate.

Results: All versions of ChatGPT passed the EUNACOM drills. Specifically, versions 4 and 4V outperformed version 3.5, achieving average accuracy rates of 79.32% and 78.83%, respectively, compared to 57.53% for version 3.5 (P<.001). Version 4V, however, did not outperform version 4 (P=.73), despite the additional visual capabilities. We also evaluated ChatGPT's performance in different medical areas of the EUNACOM and found that versions 4 and 4V consistently outperformed version 3.5. Across the different medical areas, version 3.5 displayed the highest accuracy in psychiatry (69.84%), while versions 4 and 4V achieved the highest accuracy in surgery (90.00% and 86.11%, respectively). Versions 3.5 and 4 had the lowest performance in internal medicine (52.74% and 75.62%, respectively), while version 4V had the lowest performance in public health (74.07%).

Conclusions: This study reveals ChatGPT's ability to pass the EUNACOM, with distinct proficiencies across versions 3.5, 4, and 4V. Notably, advancements in artificial intelligence (AI) have not significantly led to enhancements in performance on image-based questions. The variations in proficiency across medical fields suggest the need for more nuanced AI training. Additionally, the study underscores the importance of exploring innovative approaches to using AI to augment human cognition and enhance the learning process. Such advancements have the potential to significantly influence medical education, fostering not only knowledge acquisition but also the development of critical thinking and problem-solving skills among health care professionals.

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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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