产生医学检查的大型语言模型:系统性审查
Yaara Artsi1, Vera Sorin2,3,4, Eli Konen2,3
1Azrieli Faculty of Medicine, Bar-Ilan University, Ha'Hadas St. 1, Rishon Le Zion, Zefat, 7550598, Israel. yaara.artsi77@gmail.com.
BMC medical education
|March 30, 2024
概括
大型语言模型 (LLM) 显示出产生医学多选择题 (MCQ) 的潜力. 然而,目前人工智能生成的MCQ需要对考试有效性进行重大修订,这表明LLM最好作为补充工具使用.
科学领域:
- 医疗教育 技术 技术 医学教育
- 医疗保健中的人工智能
- 评估和评价的评估和评估.
背景情况:
- 医学教育工作者面临的挑战是为考试创建高质量的多选择题 (MCQ).
- 本系统性审查审查了大型语言模型 (LLM) 在生成医疗MCQ中的实用性.
研究的目的:
- 系统地审查LLMs在产生医疗检查的MCQ中的应用和有效性.
- 评估人工智能生成的医学MCQ的有效性和质量.
主要方法:
- 通过使用MEDLINE进行了系统的文献搜索,直到2023年11月.
- 研究的重点是医学考试的LLM生成的MCQ;非英语和无关的研究被排除在外.
- 用定制的 QUADAS-2 工具评估偏差风险,并遵循 PRISMA 的指导方针.
主要成果:
- 分析了使用GPT-3.5和GPT-4的八项研究 (2023年4月至10月).
- 五项研究表明,LLM可以产生合格的问题,但所有研究都报告了需要修改的错误或不适当的问题.
- 对比分析显示,人工智能生成的问题有时低于人类撰写的问题;两项研究存在偏见的高风险.
结论:
- 在医学MCQ的创建方面,LLM显示出有前途的帮助.
- 目前的LLM生成的MCQ需要仔细审查和修改以考试适用性.
- 需要进一步的研究来确定确的证据;目前,LLM最好被视为补充工具.
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