使用社会人工智能图像系统选社会焦虑
Qianqian Ju1, Zhijian Xu1, Zile Chen2
1School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, China.
Journal of anxiety disorders
|December 13, 2024
概括
一个使用图像的新人工智能系统,社会人工智能图像系统 (SAIPS),对选社会焦虑障碍 (SAD) 充满希望. 它准确地预测了社会焦虑特征,为传统方法提供了可靠的替代方案.
科学领域:
- 精神病学是一个精神病学.
- 人工智能的人工智能
- 心理评估 心理评估
背景情况:
- 社会焦虑障碍 (SAD) 很常见,但由于社会回避和当前的调查问卷等方法的局限性,查具有挑战性.
- 现有的选工具受到主观性,记忆偏见和文化影响的影响,突出了需要改进方法的需要.
- 早期发现SAD对于及时干预和更好的患者结果至关重要.
研究的目的:
- 开发和验证一种基于人工智能的新型工具,即社会人工智能图像系统 (SAIPS),用于可靠和可访问的社会焦虑查.
- 评估SAIPS能够捕捉社会焦虑的多方面的维度的能力,包括对负面评价的恐惧,社会互动和绩效焦虑.
- 用机器学习评估SAIPS对社会焦虑特征的预测准确度.
主要方法:
- 使用生成型多模基础AI模型开发SAIPS,包括279个社交图片和118个控制图片.
- 设计了社会场景来代表核心SAD触发因素,并包括非社会控制图像来隔离特定于社会焦虑的反应.
- 通过实验室和在线实验收集了五个维度的SAIPS参与者评分,随后进行机器学习分析.
主要成果:
- SAIPS评级表现出强大的反映和预测个人的社会焦虑特征.
- 该系统在预测社会焦虑和兴奋水平方面表现特别强.
- 即使使用SAIPS的缩短版本,包括不到30张图片,也可以实现可靠的预测.
结论:
- SAIPS显示出作为一种有价值的工具来支持对社会焦虑障碍的查的潜力.
- 与传统查方法相比,人工智能驱动的系统提供了更客观和更容易获得的方法.
- SAIPS可以促进社会焦虑的早期发现和纵向预测,帮助临床干预.
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