评估聊天机器人技术在 ортодонтических迷你工作人员的信息质量
Başak Baş Yamaç1, Rojda Akçar1, İpek Eryılmaz Şarkan1
1Istanbul Medipol University, Faculty of Dentistry, Department of Orthodontics (Istanbul, Turkey).
Dental press journal of orthodontics
|December 11, 2025
概括
大型语言模型 (LLM) 显示了在向正牙患者提供关于迷你螺丝杆 (临时杆装置) 的信息方面的潜力. 然而,来自人工智能聊天机器人的信息需要专业验证患者安全和治疗准确性.
科学领域:
- 医疗保健中的人工智能
- ортодонтика治疗技术的技术.
- 患者教育和信息传播
背景情况:
- 人工智能聊天机器人越来越多地被整合到医疗保健中,提供可访问的患者信息.
- ортодонтических患者经常询问关于迷你螺杆 (临时定装置 - TADs).
- 对于TAD等医疗主题,人工智能生成的信息的可靠性仍然是一个关键问题.
研究的目的:
- 为了比较来自四个领先的大型语言模型 (LLM) 的答案的准确性和可靠性.
- 评估人工智能聊天机器人的表现,以了解患者对 ортодонтических minicrews 的问题.
- 评估的模型:聊天GPT-3.5,聊天GPT-4,谷歌巴德和Bing聊天.
主要方法:
- 通过谷歌搜索识别了30个经常被问到的患者问题.
- 向四个不同的LLM提交选定的问题:ChatGPT-3.5,ChatGPT-4,Google Bard和Bing Chat.
- 通过使用修改的利克尔特尺度和修改的DISCERN (mDISCERN) 标准评估了三个正牙医住院的LLM反应.
主要成果:
- 聊天GPT-4获得了最高的平均Likert得分 (3.84),而Bing聊天得分最低 (3.37).
- 在评估LLM反应 (p=0.016,p<0.001,p=0.017) 中观察到显著的评审者间可靠性.
- 聊天GPT-4 (23.43 ± 2.89) 和谷歌巴德 (23.47 ± 3.2) 的mDISCERN分数比聊天GPT-3.5 (22.62 ± 2.97) 和Bing聊天 (21.63 ± 2.43) 的分数更高.
结论:
- 在教育 ортодонтических 患者关于迷你crews.LLMs显示有前途的潜力.
- 人工智能产生的信息需要验证,应该补充,而不是取代专业的牙科建议.
- 需要进一步的研究来提高AI在患者正牙教育中的准确性和安全性.
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