Stade Elizabeth C, Stirman Shannon Wiltsey, Ungar Lyle H, Boland Cody L, Schwartz H Andrew, Yaden David B, Sedoc João, DeRubeis Robert J, Willer Robb, Eichstaedt Johannes C
Dissemination and Training Division, National Center for PTSD, VA Palo Alto Health Care System, Palo Alto, CA, USA.
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Npj Ment Health Res. 2024 Apr 2;3(1):12. doi: 10.1038/s44184-024-00056-z.
Large language models (LLMs) such as Open AI's GPT-4 (which power ChatGPT) and Google's Gemini, built on artificial intelligence, hold immense potential to support, augment, or even eventually automate psychotherapy. Enthusiasm about such applications is mounting in the field as well as industry. These developments promise to address insufficient mental healthcare system capacity and scale individual access to personalized treatments. However, clinical psychology is an uncommonly high stakes application domain for AI systems, as responsible and evidence-based therapy requires nuanced expertise. This paper provides a roadmap for the ambitious yet responsible application of clinical LLMs in psychotherapy. First, a technical overview of clinical LLMs is presented. Second, the stages of integration of LLMs into psychotherapy are discussed while highlighting parallels to the development of autonomous vehicle technology. Third, potential applications of LLMs in clinical care, training, and research are discussed, highlighting areas of risk given the complex nature of psychotherapy. Fourth, recommendations for the responsible development and evaluation of clinical LLMs are provided, which include centering clinical science, involving robust interdisciplinary collaboration, and attending to issues like assessment, risk detection, transparency, and bias. Lastly, a vision is outlined for how LLMs might enable a new generation of studies of evidence-based interventions at scale, and how these studies may challenge assumptions about psychotherapy.
诸如OpenAI的GPT-4(为ChatGPT提供支持)和谷歌的Gemini等基于人工智能构建的大语言模型,在支持、增强甚至最终实现心理治疗自动化方面具有巨大潜力。该领域以及行业对这类应用的热情正在不断高涨。这些发展有望解决心理保健系统能力不足的问题,并扩大个人获得个性化治疗的机会。然而,临床心理学对于人工智能系统来说是一个风险极高的应用领域,因为负责任且基于证据的治疗需要细致入微的专业知识。本文为临床大语言模型在心理治疗中的雄心勃勃但负责任的应用提供了路线图。首先,介绍了临床大语言模型的技术概述。其次,讨论了将大语言模型整合到心理治疗中的各个阶段,同时强调了与自动驾驶汽车技术发展的相似之处。第三,讨论了大语言模型在临床护理、培训和研究中的潜在应用,鉴于心理治疗的复杂性,突出了风险领域。第四,提供了关于临床大语言模型负责任开发和评估的建议,包括以临床科学为核心、开展强有力的跨学科合作,以及关注评估、风险检测、透明度和偏差等问题。最后,概述了大语言模型如何能够大规模开展新一代基于证据的干预研究,以及这些研究可能如何挑战关于心理治疗的假设。