评估人工智能的有效性,为患有口腔静脉瘤的患者提供术前信息
Bryan Lim1, Gabriel Lirios1, Aditya Sakalkale2
1Department of Colorectal Surgery, Western Health, Melbourne, Australia.
ANZ journal of surgery
|December 2, 2024
概括
人工智能 (AI) 对口腔护理教育充满希望,但目前的大型语言模型 (LLM) 提供通用建议. 需要进一步开发在静脉管管管理中提供个性化的患者支持.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者教育 患者教育
背景情况:
- 口腔显著影响患者的生活方式和心理健康,需要强有力的教育支持.
- 现有的口腔护理教育方法有局限性,这表明人工智能 (AI) 可能发挥作用.
研究的目的:
- 检查人工智能,特别是大型语言模型 (LLM) 的实用性,以提高结肠直肠外科手术后的口腔治疗管理.
- 评估著名的LLM的适用性,作为补充性临床工具,用于牙护理.
主要方法:
- 使用可读性评分 (Flesch-Kincaid,Flesch-Reading Ease,Coleman-Liau) 和可靠性评估 (Likert 尺度,DISCERN,QAMAI) 进行了四个LLM (ChatGPT-3.5,ChatGPT-4.0,Gemini,CoPilot) 的比较.
- 进行了定性和定量分析,以评估LLM产生的建议是否适合门患者.
主要成果:
- 在LLM中观察到可读性和可靠性的显著差异;CoPilot和ChatGPT-4的表现更好.
- 虽然LLM的回应是直接的,但往往是通用的,由于无法考虑患者特定因素,因此缺乏个性化.
- 理解LLM产生的建议通常需要高中到大学水平的教育.
结论:
- 人工智能,特别是LLM,有可能改善患者教育和口腔管道管理支持.
- 目前的LLM技术还处于临床应用的早期阶段,需要仔细考虑模型功能和使用上下文.
- 解决个体患者的复杂性仍然是人工智能系统在静脉关闭护理中的挑战.
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