临床基准的上下文化:一种三方方法来评估在心理健康环境中基于LLM的工具
Matthew Flathers1, Bridget Dwyer, Eden Rozenblit
1The Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
Journal of psychiatric practice
|December 1, 2025
概括
本研究为评估心理健康护理中的大型语言模型 (LLM) 工具提供了一个实用的框架. 它提供了即时的评估方法,以确保人工智能 (AI) 提高患者安全和临床结果.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 心理健康技术 心理健康技术
背景情况:
- 大型语言模型 (LLM) 在心理健康领域的广泛采用需要强有力的临床评估.
- 现有的人工智能 (AI) 工具评估方法往往是理论性的,并不能立即适用于临床环境.
- 迫切需要实用,可实施的框架来评估LLM在心理健康护理中的安全性和有效性.
研究的目的:
- 引入一个实用的,三方评估框架,以LLM为基础的工具在心理健康.
- 为立即临床实施提供可操作的评估方法.
- 建立一个基础,为AI在心理健康领域开发专门的基准.
主要方法:
- 一个三层的框架:技术概况,医疗保健知识和临床推理.
- 评估方法包括直接模型质询和对抗性测试.
- 专注于临床团队立即实施的可能性.
主要成果:
- 拟议的框架为评估LLM工具提供了立即的实际指导.
- 它涉及基础安全,特定领域的知识和临床推理能力.
- 这种方法有助于开发人工智能在心理健康方面的基本基准.
结论:
- 三方框架为评估心理健康护理中的LLM工具提供了一种可行的方法.
- 立即实施这些方法可以提高患者的护理和安全.
- 该框架支持集体开发专门的AI评估资源,用于心理健康.
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