以人工智能为指导的数字干预与生理监测可以减少实验性创伤后的侵入性记忆
Megan T deBettencourt1, Sruthi Sakthivel2, Emily A Holmes3,4
1Ruby Neurotech, Redwood City, CA, USA. megan@ruby-neurotech.com.
NPJ digital medicine
|December 26, 2025
概括
生成型人工智能和神经技术为创伤治疗提供了一个可扩展的替代方案. 这项研究表明,人工智能引导的数字干预有效地减少了侵入性记忆,与瞳孔大小跟踪参与.
科学领域:
- 数字心理健康干预数字心理健康干预
- 在心理学中的人工智能.
- 神经技术应用的神经技术应用.
背景情况:
- 创伤的全球流行需要可扩展的治疗解决方案.
- 目前的数字治疗通常需要人类指导,限制了可扩展性.
- 生成型人工智能和神经技术为自动化,可扩展的干预提供了潜力.
研究的目的:
- 测试ANTIDOTE,这是一个结合人工智能指导和瞳孔测量的新系统.
- 评估人工智能交付的图像竞争任务干预 (ICTI) 的有效性,以减少创伤后的侵入性记忆.
- 探索瞳孔测量作为干预参与和减轻症状的生物标志物.
主要方法:
- 一百名健康的志愿者被暴露在创伤性视频中.
- 参与者被随机分配到人工智能引导的ICTI或积极对照组.
- 瞳孔测量用于监测干预期间的参与.
主要成果:
- 与对照组相比,干预参与者报告的侵入性记忆显著减少.
- 人工智能指南成功地按照预期提供了ICTI.
- 瞳孔大小与干预参与度相关,并与症状减少有关.
结论:
- 人工智能引导的数字干预,比如Antidote,有望有效地减少创伤后的侵入性记忆.
- 瞳孔测量可以作为参与数字心理健康干预的生物标志物.
- 这种方法为提供基于证据的创伤治疗提供了一个可扩展的途径.
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