反响智能:评估新型生成人工智能的能力与心脏病学教育中的大型语言模型
Muneeb Ahmed1, Flora Huang2, Chi-Ming Chow1
1Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
CJC open
|June 30, 2025
概括
人工智能 (AI) 可以创建高质量的心脏病学教育材料,与人类专家相美. 医学学员无法区分人工智能产生的问题和人类产生的问题,这表明AI在医学教育中的潜力.
科学领域:
- 医学教育 医学教育
- 人工智能的人工智能
- 心脏病学 心脏病学
背景情况:
- 人工智能 (AI) 显示出改善医学教育的潜力.
- 这项研究侧重于人工智能在心脏病学等复杂领域的作用.
- 评估了用于生成和评估教育内容的大型语言模型 (LLM).
研究的目的:
- 评估人工智能模型在心脏病学中生成教育材料的能力.
- 将人工智能生成的内容质量与人类专家进行比较.
- 评估医疗学员区分人工智能产生的内容与人类产生的内容的能力.
主要方法:
- 一个人工智能模型在80个心脏病学讲座中接受了培训.
- 人工智能产生了10个多选题 (MCQ);心脏病学家产生了10个.
- 心脏病学实习生回答了问题并试图确定来源;AI也回答了.
主要成果:
- 实习生在人工智能生成的MCQ和人类生成的MCQ中得分相似 (两者的中位数为8/10).
- 实习生无法可靠地区分人工智能产生的问题和人类产生的问题 (中位数为10/20的正确识别).
- 人工智能表现出高准确度 (95%在自己的问题上,100%在人类问题上).
结论:
- 人工智能产生的教育内容在质量上与人类专家制作的内容相提并论.
- 医学实习生无法可靠地区分人工智能产生的和人类产生的心脏病问题.
- 人工智能具有显著的潜力,通过提供可扩展,高质量的学习资源来增强心脏病学教育.
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