在医学诊断中对多模式数据和人工智能技术的协同作用进行全面审查
Xi Xu1, Jianqiang Li1, Zhichao Zhu1
1Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China.
Bioengineering (Basel, Switzerland)
|March 27, 2024
概括
人工智能 (AI) 通过整合多模式数据来增强疾病诊断. 这项研究调查了人工智能.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 计算生物学 计算生物学
背景情况:
- 疾病诊断是复杂的,需要整合各种数据,如图像,文本和遗传信息.
- 人工智能 (AI) 提供先进的解决方案,以支持医生做出基于证据的诊断决策.
- 多模式数据分析与人工智能相结合,是研究人员和临床医生的快速增长的兴趣领域.
研究的目的:
- 调查用于诊断特定疾病的多模式数据和人工智能的最新进展.
- 为阿尔茨海默病,乳腺癌,抑郁症,心脏病和的AI驱动的诊断方法提供全面的概述.
- 突出人工智能辅助医学诊断方面的挑战和未来方向.
主要方法:
- 综述当前关于人工智能在医学诊断中的应用在多种数据模式上的文献.
- 专注于五种特定疾病:阿尔茨海默病,乳腺癌,抑郁症,心脏病和.
- 对诊断方法,公共数据集,特征工程和分类模型的分析.
主要成果:
- 详细的诊断方法使用AI跨各种数据类型选择疾病.
- 识别常用的公共数据集和特征工程技术.
- 概述流行的人工智能分类模型及其性能.
结论:
- 人工智能,特别是多模式方法,显示出改善疾病诊断和临床决策的巨大潜力.
- 需要进一步的研究来解决数据整合,模型解释性和临床实施方面的挑战.
- 这项调查为推进医疗诊断中的AI提供了有价值的见解.
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