基于TinyML的轻量级AI 医疗保健 移动聊天机器人 部署
Anita Christaline Johnvictor1, M Poonkodi1, N Prem Sankar1
1School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India.
Journal of multidisciplinary healthcare
|November 14, 2024
概括
这项研究介绍了一种安全的AI医院聊天机器人,使用TinyML进行高效的患者数据处理. 它通过准确的数据记录和通过本地服务器操作提高安全性来增强患者护理.
科学领域:
- 医疗保健中的人工智能
- 边缘计算 边缘计算
- 机器学习用于医疗应用.
背景情况:
- 人工智能 (AI) 正在改变医疗保健,改善患者的互动和满意度.
- 在线人工智能解决方案对敏感患者数据安全构成风险.
- 人工智能的进步对护理,诊断和关键医疗程序产生影响.
研究的目的:
- 为医院环境开发一个安全,人工智能驱动的聊天机器人.
- 使用本地处理解决患者数据安全问题.
- 提高患者数据管理和诊断支持的效率.
主要方法:
- 利用边缘计算来进行安全的现场数据处理.
- 实现基于梯度 (HOG) 的基底组图 (Histogram of Gradient) 的分类,用于患者识别.
- 集成的TinyML用于有效处理患者数据,包括温度测量和通过聊天机器人进行人口统计记录.
主要成果:
- 在患者检测方面获得了95.8%的准确性,TinyML模型的准确性为95.3%.
- 证明了快速的设备处理时间 (217 ms) 和低延迟 (4 ms).
- 在TinyML模型需要最小的资源 (8.8Kb RAM,50.3Kb闪存).
结论:
- 与现有的AI解决方案相比,开发的系统提供了增强的安全性和更快的患者数据记录.
- 所有数据处理都发生在本地,确保患者数据的隐私和完整性.
- 人工智能聊天机器人为医院管理和诊断目的提供安全的数据存储.
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