人工智能辅助的心理健康临床面试
Sverker Sikström1,2, Rebecca Astrid Boehme3,4, Mariam Mirström3
1Department of Psychology, Lund University, Lund, Sweden. sverker.sikstrom@psy.lu.se.
Scientific reports
|October 30, 2025
概括
人工智能驱动的临床面试准确地评估心理健康障碍,表现优于传统的尺度. 这项技术提供了一个可扩展,具有成本效益和同情心的精神卫生保健方法,补充了现有的诊断方法.
科学领域:
- 数字健康数字健康
- 人工智能在医学中的应用
- 精神病学是一个精神病学.
背景情况:
- 传统的心理健康评估依赖于临床医生的采访,这些采访是昂贵的,耗时的,缺乏标准化.
- 大型语言模型 (LLM) 提供了开发可以模拟这些采访的AI系统的机会,解决了当前的局限性.
- 对临床面试人工智能工具的严格验证很少,这凸显了对经验证据的需求.
研究的目的:
- 开发和评估一个人工智能助手TalkToAlba,用于根据DSM-5标准进行临床采访.
- 与既有方法相比,评估人工智能驱动的诊断访谈的准确性,可靠性和用户体验.
主要方法:
- 一名人工智能助理对303名参与者进行了诊断采访,他们报告有各种心理健康障碍 (MDD,GAD,OCD,PTSD,ADD/ADHD,ASD,ED,SUD,BD) 和健康对照.
- 一个二级AI系统分析了成绩单,以验证诊断标准并证明结论的合理性.
- 人工智能面试的表现与已建立的评级尺度进行了比较,使用了诸如科恩的卡帕,灵敏度和特异性等指标.
主要成果:
- 与传统评级表相比,人工智能驱动的采访显示出在识别自我报告的疾病方面具有更高的一致性,敏感性和特异性.
- 人工智能系统显示,诊断类别之间的相互依赖性减少了.
- 参与者在人工智能驱动的采访中报告了高度的同情,相关性,理解和支持.
结论:
- 人工智能支持的临床面试是评估常见精神障碍的准确,标准化和以人为中心的工具.
- 这些人工智能工具提供了可扩展性,低成本和积极的用户体验,将它们定位为传统诊断的有价值补充.
- 这些发现支持在精神卫生保健服务中广泛采用人工智能驱动的采访的可能性.
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