人的声音作为一个数字健康解决方案,利用人工智能
Pratyusha Muddaloor1, Bhavana Baraskar2, Hriday Shah3
1Department of Internal Medicine, Lower Bucks Hospital, Bristol, PA 19007, USA.
Sensors (Basel, Switzerland)
|September 19, 2025
概括
使用人工智能 (AI) 的语音分析提供了一个新的诊断工具. 机器学习模型擅长识别各种疾病的语音生物标志物,增强医疗保健洞察力和患者隐私.
科学领域:
- 计算语言学 计算语言学
- 生物医学信息学 生物医学信息学
- 医疗保健中的人工智能
背景情况:
- 人的声音作为一种重要的沟通媒介,反映了情绪状态.
- 声乐障碍可以被分析为疾病诊断的潜在生物标志物.
- 交谈式人工智能 (AI) 为人机交互提供了语音支持的技术.
研究的目的:
- 审查用于用于诊断目的的语音分析的人工智能 (AI) 模型.
- 探索声乐生物标志物的结果和临床应用.
- 解决伦理方面的考虑,特别是数据隐私和安全.
主要方法:
- 从音频录音中提取相关的声乐特征.
- 使用神经网络和机器学习 (ML) 模型分析提取的特征.
- 将ML模型与传统的光谱分析技术进行比较.
主要成果:
- 机器学习模型在整合复杂的声音特征数据方面表现出优于光谱分析的优势.
- 声乐生物标志物在诊断神经系统疾病 (如帕金森氏症,阿尔茨海默氏症),心理疾病和其他疾病 (如DM,CHF,CAD,GERD,肺部疾病,COVID-19) 中具有潜力.
- 加密方法可以减轻与患者可识别的语音数据相关的隐私和安全问题.
结论:
- 人工智能驱动的语音分析是一个有前途的非侵入性诊断工具.
- 声乐生物标志物的整合到医疗保健中可以显著提高预测分析和疾病管理.
- 人工智能的持续进步正在扩大语音作为数字健康解决方案的潜力,隐私保护至关重要.
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