走向隐私意识的心理健康人工智能模型
Aishik Mandal1,2, Tanmoy Chakraborty3,4, Iryna Gurevych5,6
1Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt, Darmstadt, Germany.
Nature computational science
|October 10, 2025
概括
人工智能 (AI) 可以帮助诊断心理健康障碍,但隐私是一个问题. 这项研究提出了保护隐私的人工智能解决方案,以改善心理健康护理的可访问性和结果.
科学领域:
- 人工智能的人工智能
- 心理健康 心理健康
- 计算神经科学是一种神经科学.
背景情况:
- 心理健康障碍带来了重大的个人和社会挑战.
- 传统的诊断方法往往是资源密集型的,缺乏可访问性.
- 新兴的人工智能技术,包括NLP和多式联络方法,显示出心理健康检测的潜力.
研究的目的:
- 探索人工智能驱动的心理健康诊断中的挑战,并为隐私问题提出解决方案.
- 为临床决策推进可靠且对隐私感知的人工智能工具的开发.
主要方法:
- 检查与心理健康中的AI相关的隐私挑战.
- 建议解决方案,如数据匿名化,合成数据生成和保护隐私的培训方法.
- 制定平衡隐私和数据实用性的框架.
主要成果:
- 在将人工智能应用于敏感心理健康数据时,确定了关键的隐私风险.
- 在AI模型开发中展示了隐私保护技术的可行性.
- 概述了管理隐私-实用性权衡的结构化方法.
结论:
- 人工智能为改善心理健康诊断和可访问性提供了有希望的途径.
- 通过强有力的解决方案解决隐私问题对于道德的人工智能实施至关重要.
- 拟议的框架可以指导开发可靠的人工智能工具,以加强心理健康护理.
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