对人类骨学习的生成人工智能工具进行比较研究
Wachirawit Sirirat1, Paak Rewthamrongsris1,2, Kritchai Bespinyowong1,2
1Center of Artificial Intelligence and Innovation (CAII) and Centre of Excellence for Dental Stem Cell Biology, Faculty of Dentistry, Chulalongkorn University, Bangkok, Thailand.
像ChatGPT-4o,Claude 3.7 Sonnet和Gemini 2.0 Flash这样的生成性AI工具在人类骨解剖学教育中显示出较低的准确性和一致性. 这些人工智能模型对于解剖学学习是不可靠的,原因是经常出现的不准确性.
科学领域:
- 医疗教育 技术 技术 医学教育
- 人工智能在解剖学中的作用
- 数字学习工具 数字学习工具
背景情况:
- 人类骨解剖学的自主学习对医学学生至关重要.
- 生成型人工智能工具提供了教育支持的潜力,但需要验证.
- 在临床应用之前,评估AI的准确性和一致性至关重要.
研究的目的:
- 评估ChatGPT-4o,克劳德3.7索内特和双子座2.0闪光在人类骨解剖学的有效性.
- 确定这些人工智能工具的准确性和响应一致性,以实现自我指导的解剖学学习.
- 确定用于解剖教育的生成AI的可靠性.
主要方法:
- 评估了143个人类骨标本和715张图像.
- 生成了4个问题类型来测试人工智能对解剖特征的识别.
- 评估了响应的准确性 (正确,不正确,无法指定,无法分析) 和一致性 (105张图像进行了5次试验).
主要成果:
- 聊天GPT-4o实现了最高的精度 (44.75%),超过了克劳德3.7索内特和双子座2.0闪光.
- 人工智能模型之间的一致性从轻微到相当 (科恩的 κ).
- 双子座 2.0 Flash 产生了"不能分析"的响应;它具有最高的一致性 (62.86%的相同响应). 克劳德3.7小说显示了最不一致的反应.
结论:
- 目前的生成AI模型缺乏有效解剖教育所需的可靠性.
- 这些人工智能工具表现出产生不准确解剖信息的高度倾向.
- 在人类骨解剖学中使用生成人工智能进行自主学习时,建议谨慎使用.
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