绘制人工智能心理健康聊天机器人的演变图,从基于规则的系统到大型语言模型:系统性审查
Yining Hua1,2, Steve Siddals2, Zilin Ma3
1Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
概括
在心理健康护理中的人工智能 (AI) 聊天机器人缺乏严格的临床证据. 一项审查强调了需要标准化的评估框架,以确保在心理健康领域安全有效地部署人工智能.
科学领域:
- 数字心理健康数字心理健康
- 医疗保健中的人工智能
- 临床评估方法 临床评估方法
背景情况:
- 人工智能聊天机器人在心理健康护理中的普及,其特点是证据碎片化和评估标准不一致.
- 现有的研究往往缺乏严谨性,因此很难确定这些技术的真正临床效用和安全性.
研究的目的:
- 系统地审查和分类用于心理健康护理的AI聊天机器人架构.
- 提出一个标准化的三层评估框架来评估人工智能聊天机器人的性能,从技术验证到临床有效性.
- 确定AI聊天机器人的评估中的关键差距,特别是关于临床益处和道德考虑的问题.
主要方法:
- 2020年至2024年间发表的160项关于人工智能聊天机器人在心理健康方面的研究的系统审查.
- 聊天机器人架构的分类:基于规则,基于机器学习和基于大型语言模型 (LLM).
- 开发和应用三级评估框架:基础基准测试,试点可行性测试和临床疗效测试.
主要成果:
- 基于大型语言模型 (LLM) 的聊天机器人在新研究中迅速增加 (2024年为45%),从早期基于规则的统治地位转变.
- 在临床疗效测试中存在很大的差距,只有47%的研究专注于它,只有16%的LLM研究达到这一阶段.
- 在市场上的功能和实际的人工智能架构之间存在差异,许多系统使用了基于规则的基本脚本.
结论:
- 人工智能心理健康工具的标准化评估和基准测试,与医疗人工智能认证标准保持一致,至关重要.
- 拟议的框架为临床医生,研究人员和监管机构提供了一个结构化的方法,以指导安全,透明和道德地部署AI聊天机器人.
- 未来的研究必须优先考虑严格的临床疗效试验,AI架构的透明报告和现实世界的影响评估,以确保AI有效地增强心理健康护理.
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