人工智能语言模型可以有效地解决牙创伤问题吗?
Hasibe Elif Kuru1, Aslı Aşık2, Doğukan Mert Demir3
1Faculty of Dentistry, Department of Pediatric Dentistry, Uşak University, Uşak, Turkey.
概括
大型语言模型 (LLM) 显示了牙科创伤教育的潜力,但它们的准确性各不相同. 聊天GPT 3.5表现最好,而双子座扎,强调需要仔细整合人工智能在医疗保健教育.
科学领域:
- 医疗保健中的人工智能
- 牙科创伤学教育 牙科创伤学教育
- 自然语言处理应用程序
背景情况:
- 人工智能 (AI) 聊天机器人或大型语言模型 (LLM) 越来越多地被采用为医疗保健机构的教育资源.
- 在紧急牙科创伤教育中,LLM的应用越来越多,需要对其可靠性和准确性的评估.
研究的目的:
- 为了比较不同大型语言模型 (LLM) 的可靠性和准确性,在回答有关牙科创伤的查询时.
- 为了评估五个不同的LLM的性能:ChatGPT 4,ChatGPT 3.5,Copilot Free,Copilot Pro和谷歌双子.
主要方法:
- 一项横截面的观察性研究涉及向五名LLM提出30个基于国际牙科创伤学协会指导方针的问题.
- 问题包括多项选择,填空和二分法格式,连续9天进行.
- 通过使用统计测试,分析了答案的正确性和一致性,显著程度为p < 0.05.
主要成果:
- 所有LLM在回答重复问题方面都表现出高度的重复性.
- 聊天GPT 3.5取得了最高的成功率 (76.7%),其次是Copilot Pro (73.3%),Copilot Free (70%),聊天GPT 4 (63.3%) 和双子座 (46.7%).
- 聊天GPT 3.5,聊天GPT 4,和双子座在多选项和填空问题上表现明显好于二分法;Copilot模型在不同类型的问题上没有显著差异. 从Copilot和Gemini的解释和引用通常是不准确的.
结论:
- 大型语言模型 (LLM) 具有作为牙科创伤学中补充教育工具的潜力.
- 可变的准确性和包含不可靠的参考资料需要谨慎的实施策略在牙科教育LLMs.
- 需要进一步的研究来优化AI在临床教育中的使用.
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