探索生成人工智能辅助医学教育:评估医学学生的基于案例的学习
Matthew Sauder1, Tara Tritsch1, Vijay Rajput1
1Medical Education, Dr. Kiran C. Patel College of Allopathic Medicine, Nova Southeastern University, Fort Lauderdale, USA.
Cureus
|February 9, 2024
概括
生成型人工智能 (GenAI) 在医学教育方面表现有前途,但存在局限性. 教师必须指导学生如何有效地使用像ChatGPT这样的工具,并认识到它有可能遗漏关键信息.
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
背景情况:
- 生成型人工智能 (GenAI) 越来越容易获得医学教育.
- 确定GenAI在医学院课程中的最佳作用对于学生和教师来说至关重要.
- 了解GenAI的能力和局限性对于有效的集成至关重要.
研究的目的:
- 评估GenAI,特别是ChatGPT在职前医疗课程中的能力.
- 评估GenAI在生成教育材料,评估问题和回答医疗问题方面的表现.
- 通过基于问题的学习场景探索GenAI在临床推理中的作用.
主要方法:
- 评估了ChatGPT在生成教育内容方面的表现.
- 评估其创建医学评估问题的能力.
- 在基于案例的积极学习框架内测试了它对医学查询和临床推理场景的响应.
主要成果:
- 基因人工智能有效地解决了流行病学,诊断和治疗方面的问题.
- 观察到不一致的答案全面性;关键细节有时被遗漏.
- 基因AI在复杂的主题上扎,这给学生带来不完整信息的风险.
结论:
- 基因人工智能可以成为医学教育中的一个有价值的工具,但需要仔细监督.
- 教师意识到GenAI的优点和弱点对于指导学生使用至关重要.
- 为确保准确的信息检索和防止知识差距,教育学生了解GenAI的局限性至关重要.
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