心理健康护理中的生成多式大型语言模型:应用,机遇和挑战
Ariel Soares Teles1,2, Jaya Chaturvedi1, Tao Wang1
1Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
PLOS mental health
|February 9, 2026
概括
多模大型语言模型 (MLLMs) 集成各种数据,如语音和图像,以增强心理健康护理. 这种方法提供了比文字更丰富的洞察力,以改善诊断和个性化干预.
科学领域:
- 人工智能的人工智能
- 心理健康技术 心理健康技术
- 计算语言学 计算语言学
背景情况:
- 传统的大型语言模型 (LLM) 在基于文本的任务中表现出色,但缺乏解释心理健康复杂多维性质的能力.
- 心理健康评估通常需要整合文本之外的各种来源的信息,包括非语言线索和生理数据.
研究的目的:
- 探索多式大型语言模型 (MLLMs) 在心理健康护理中的新兴作用.
- 区分MLLM与传统的纯文本LLM,并概述其基本原则.
- 综合当前的研究和在心理健康环境中MLLM的应用.
主要方法:
- 这篇叙事综述综合了最近的经验研究和MLLM在心理健康中的实验应用.
- 该综述概述了MLLM的基本原则,并将其与纯文本LLM进行对比.
- 它检查了各种输入方式的整合,如语音,图像,视频和生理信号.
主要成果:
- 通过结合多方面的数据,MLLM显示了提高心理健康诊断准确性的潜力.
- 这些模型可以实现心理状态的实时监测,并促进情境意识,个性化干预.
- 最近的研究强调了MLLM能够理解超越文本内容的微妙的人际交互的能力.
结论:
- 在人工智能驱动的心理健康护理中,MLLMs代表了显著的进步,提供了更全面的方法.
- 需要进一步的研究和创新来应对实施挑战,并充分发挥MLLM的潜力.
- 通过MLLM集成各种数据模式,有望提高心理健康服务的有效性和个性化.
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