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通过适应人类专家的诊断特征,为临床诊断提供人机合作元模型
Hae-Jeong Park1,2,3, Sung Huhn Kim4, Jae Young Choi4
1Department of Nuclear Medicine, Department of Psychiatry, Graduate School of Medical Science, Brain Korea 21 Project, Yonsei University College of Medicine, Seoul, South Korea. parkhj@yonsei.ac.kr.
Scientific reports
|September 27, 2023
概括
本研究介绍了一种适应性人工智能 (AI) 的元模型,用于医学诊断. 人工智能学习个人临床医生的倾向,以提高诊断准确度,改善临床环境中的人机合作.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 临床决策支持 临床决策支持
背景情况:
- 深度学习人工智能 (AI) 接近医学图像诊断中的人类专家能力.
- 人工智能在临床环境中的作用通常仅限于助理,因为担心责任.
- 有效的AI整合需要适应个体临床医生的专业知识和决策变化.
研究的目的:
- 开发一个人机合作的元模型,使人工智能适应个人医学专家.
- 通过理解和整合独特的临床医生特征来增强AI的诊断援助.
- 在现实世界临床实践中提高人工智能辅助医学诊断的可靠性和准确性.
主要方法:
- 引入了一个元模型,使用条件概率评估专家类特定的诊断趋势.
- 该元模型根据个人专家诊断模式调整AI预测.
- 使用耳内镜数据将元模型应用于耳部疾病诊断.
主要成果:
- 整合个体诊断特征显著提高了AI性能,即使数据有限.
- 最高的诊断准确度是通过优化权衡个人专家条件概率与机器分类概率来实现的.
- 量身定制的模型在诊断耳部疾病方面表现更好.
结论:
- 一个元模型有效地将人工智能适应个人临床医生的专业知识,提高诊断准确度.
- 这种方法可以减轻人工智能建议的心理影响,并利用临床医生的独特优势.
- 个性化的AI模型对于临床决策中有效的人机合作至关重要.
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