利用语音生物标志物量化慢性疼痛:快速审查
Leah Tobey-Moore1,2, Anu Iyer3, Cade Wilkerson4
1Department of Psychiatry, Center for Health Services Research, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, AR, United States.
Frontiers in pain research (Lausanne, Switzerland)
|January 28, 2026
概括
基于语音的人工智能 (AI) 提供了一种有希望的客观方法,用于使用声乐生物标志物评估慢性疼痛. 这项技术可以改善疼痛量化,特别是对于有沟通困难的人来说,增强慢性疼痛管理.
科学领域:
- 生物医学工程 生物医学工程
- 计算语言学 计算语言学
- 数字健康数字健康
背景情况:
- 慢性疼痛评估严重依赖于主观的自我报告,缺乏准确性和可重复性.
- 人工智能 (AI) 正在彻底改变医疗保健,在疾病检测和管理方面显示出潜力.
- 需要客观的疼痛评估方法,特别是在有沟通障碍的人群中.
研究的目的:
- 审查基于语音的AI在慢性疼痛客观评估中的新兴作用.
- 探索声乐生物标志物对于疼痛量化的潜力.
- 确定AI在慢性疼痛管理中的挑战和未来方向.
主要方法:
- 对基于语音的AI和慢性疼痛研究的快速文献综述.
- 分析与疼痛相关的声乐生物标志物 (音调,大声,动).
- 对用于声数据用于疼痛检测的机器学习模型的检查.
主要成果:
- 语音生物标志物与疼痛强度和质量相关,提供客观的见解.
- 人工智能驱动的语音分析在检测疼痛方面表现有前途,特别是在弱势群体中.
- 基于人工智能的语音分析可以补充现有的疼痛管理策略,如认知行为疗法.
结论:
- 基于语音的AI提供了一个可扩展的,非侵入性的替代主观疼痛量表.
- 需要进一步的研究来标准化AI语音分析用于临床使用.
- 整合AI语音生物标志物可以改善早期诊断和个性化慢性疼痛护理.
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