一个基于聊天机器人的问答系统,用于基于大型语言模型的慢性疾病辅助诊断
1Graduate School of Communication Design, Hanyang University, ERICA Campus, Ansan, 15588, Republic of Korea. shania123@hanyang.ac.kr.
Scientific reports
|July 25, 2024
概括
这项研究开发了一个人工智能诊断系统,Chat Ella,使用GPT-2和慢性疾病的深度学习. 该系统实现了97.50%的准确性,为早期疾病检测提供了方便的工具.
科学领域:
- 人工智能的人工智能
- 医疗信息学 医疗信息学
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 通过深度学习驱动的智能聊天机器人在医疗保健方面表现有前途.
- 人工智能在诊断慢性疾病中的应用是一个尚未探索的领域.
- 现有的AI工具在慢性疾病诊断方面存在局限性.
研究的目的:
- 设计和开发一种人工智能驱动的慢性疾病诊断系统.
- 为了利用大型语言模型 (GPT系列) 和深度学习来诊断疾病.
- 为诊断系统创建一个用户友好的,基于对话的界面.
主要方法:
- 转移学习和GPT-2模型的微调.
- 开发了一个以对话为基础的接口,命名为Chat Ella.
- 通过用户描述的症状,对24种常见的慢性疾病进行系统训练.
主要成果:
- 诊断系统在验证集上实现了97.50%的准确性.
- 该模型得到了 99.91% 的曲线下面积 (AUC).
- 用户满意度测试显示68.7%的人赞成,而45.3%的人认为它适合日常咨询.
结论:
- 开发的AI系统在诊断慢性疾病方面表现出高准确度.
- 聊天艾拉提供了一个有价值的,用户友好的工具,家庭医疗咨询.
- 该系统具有将其集成到医疗辅助产品中的巨大潜力.
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