深度学习技术在医疗保健系统中的应用:一篇综述
Tayyip Ozcan1, Ahmet Nusret Toprak1, İbrahim Aruk1
1Department of Computer Engineering, Erciyes University Faculty of Engineering, Kayseri, Türkiye.
概括
人工智能 (AI) 通过诊断和药物发现等应用来增强医疗保健. 本综述侧重于深度学习方法,包括CNN,SAE和RNN,用于医疗图像分析和患者监测.
科学领域:
- 医疗保健技术 技术 医疗保健 技术
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 人工智能 (AI) 在机器中模拟人类智能.
- 人工智能在医疗保健中越来越多地用于诊断,治疗和药物发现.
- 深度学习是AI的一个子集,利用CNN,SAE和RNN等方法.
研究的目的:
- 审查深度学习方法在医疗保健中的应用.
- 强调在医疗环境中使用CNN,SAE和RNN.
- 关于AI在医学成像,药物发现和远程监测方面的研究.
主要方法:
- 对医疗保健中的深度学习现有文献的审查.
- 专注于卷积神经网络 (CNN) 用于图像分析.
- 包括用于特征学习的堆叠自动编码器 (SAE) 和用于顺序数据分析的循环神经网络 (RNN).
主要成果:
- 深度学习方法在医学图像分析中显示出显著的实用性.
- 人工智能应用加速了药物发现和开发过程.
- 通过人工智能驱动的数据分析来增强远程患者监控.
结论:
- 深度学习方法是现代医疗保健中的多功能工具.
- 人工智能整合有望在诊断,治疗和患者护理方面取得进展.
- 对人工智能应用的进一步研究可以彻底改变医学实践.
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