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人工智能和机器学习在精神病学中的一个警告故事

Zhe Sage Chen^1,2,3, Katharina Schultebraucks^4,5,6, Wei Wu⁷

¹Department of Psychiatry, New York University Grossman School of Medicine, New York, NY, 10016, USA. zhe.chen@nyulangone.org.

Translational psychiatry

|March 7, 2026

概括

人工智能 (AI) 和机器学习 (ML) 在心理健康方面表现有前途,但面临实施挑战. 人类辅助的人工智能框架和改进的数据共享是可靠精神病学工具的关键.

科学领域:

精神病学是一个精神病学.
人工智能的人工智能
机器学习机器学习

背景情况:

人工智能和机器学习已经显著提升了心理健康应用.
人工智能在精神病学中的临床转化面临着巨大的障碍.
在研究严谨性,可靠性,可解释性,实用性和伦理学上存在局限性.

研究的目的:

检查精神病学AI研究中的关键挑战.
提出人类辅助的人工智能框架来克服局限性.
确定成功在心理健康领域实施人工智能的重要举措.

主要方法:

对当前在心理健康中的AI进行了基于视角的分析.
讨论研究严谨性,可靠性,可解释性,实用性和伦理方面的局限性.
关于人类辅助人工智能框架和支持倡议的建议.

主要成果:

目前的精神科人工智能研究在严谨性,可靠性,可解释性,实用性和伦理方面面临限制.
人类辅助的人工智能框架可以提高透明度,解决偏见并建立信任.
临床教育,文化适应和数据共享对于人工智能采用至关重要.

结论:

弥合人工智能潜力和临床实践之间的差距需要解决已确定的障碍.
人类辅助的人工智能框架对于开发可靠和有效的精神病学工具至关重要.
专注于透明度,可重复性和文化适应性将指导在心理健康护理中的道德人工智能实施.

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