基于聊天的人工智能模型在区分状和高变性痕之间的潜力:试点研究
Makoto Shiraishi1, Shimpei Miyamoto2, Hakuba Takeishi2
1Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. shiraishi-kyf@umin.ac.jp.
Aesthetic plastic surgery
|September 25, 2024
概括
大型语言模型显示了痕诊断的潜力,GPT-4在准确性方面超过了Bing Chat. 然而,当前的AI诊断工具需要进一步开发用于临床使用.
科学领域:
- 皮肤病学中的人工智能
- 医学图像分析 医学图像分析
- 在医疗保健中的自然语言处理.
背景情况:
- 状体和缩性痕显著影响患者的生活质量.
- 目前的痕诊断标准复杂,导致诊断不足.
- 需要更容易获得的痕诊断方法.
研究的目的:
- 评估大型语言模型 (LLM) 对痕状况的诊断能力.
- 探索AI聊天机器人如ChatGPT在痕诊断中的应用.
- 提出一种简化痕诊断方法.
主要方法:
- 测试了五个人工智能聊天机器人 (ChatGPT-4,Bing聊天模式,Bard).
- 标准化提示被用来分析30个模拟痕图像.
- 通过对每个聊天机器人进行五次查询来评估诊断准确性.
主要成果:
- 与Bing Chat (22.0%) 相比,ChatGPT-4的痕诊断准确度显著更高 (36.0%).
- GPT-4在识别状体和高性痕方面表现出卓越的特异性.
- 在GPT-4和Bing聊天性能之间观察到一个统计学上显著的差异 (P = 0.027).
结论:
- 目前的LLM对痕诊断有前途,但尚未适用于临床应用.
- 为了满足医学诊断的临床标准,人工智能的进一步进步是必要的.
- 人工智能有可能改善痕状况的诊断.
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