从算法到操作:人工智能在教育患者进行外部鼻提升方面有多可靠?
Selin Gaş1, Gülfem Özlü Uçan2, Serap Karakış Akcan3
1Associate Professor, Department of Oral and Maxillofacial Surgery, Faculty of Dentistry, Istanbul Gelisim University, Istanbul, Turkey.
概括
大型语言模型 (LLM) 在解释外部鼻升起方面具有价值,不同的模型在特定领域表现出色. 只有解码器的LLM (DO-LLM) 更好地详细说明程序和风险,而基于变压器的LLM (TB-LLM) 提供了优质的生活方式建议.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 口腔和牙面部外科手术
背景情况:
- 患者越来越多地使用像ChatGPT和Claude这样的大型语言模型 (LLM) 来获取外科信息.
- 人工智能产生的医疗信息的准确性,质量和可读性,特别是对于像外部鼻提升这样的手术,尚不清楚.
- 本研究评估了两种不同的患者教育内容的LLM架构.
研究的目的:
- 为了比较两个AI语言模型 (DO-LLM和TB-LLM) 的响应的可靠性,质量,有用性和可读性.
- 评估人工智能生成的关于外部鼻提升程序的患者信息.
- 确定当前的LLM对于专业牙科手术患者教育的适用性.
主要方法:
- 一个横截面的研究,比较只有解码器的LLM (DO-LLM) 和基于变压器的LLM (TB-LLM).
- 对这两种模型提出了72个关于外侧鼻提升的标准化患者问题.
- 牙科专家使用修改的DISCERN,全球质量评分,实用性尺度和可读性指数 (Flesch阅读易度,Flesch-Kincaid等级) 来评估答案.
主要成果:
- 在程序和与风险相关的领域 (例如,修改的DISCERN分数) 中,DO-LLM表现出更高的可靠性.
- 结核病LLM在提供生活方式和行为建议方面表现出卓越的表现 (全球质量评分).
- DO-LLMs产生了更多的中等质量的响应 (56.9%),而TB-LLMs产生了更高的优质响应百分比 (29.2%).
结论:
- 这两种AI模型都有可能在外部鼻提升方面为患者提供教育,但具有特定领域的优势.
- DO-LLM 在解释外科手术程序和风险方面表现出色,而TB-LLM 在生活方式指导方面更好.
- 进一步开发牙科特定的AI工具和以患者为中心的整合对于优化AI在外科患者教育中至关重要.
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