Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation.

作者信息

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

机构信息

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.

出版信息

Npj Ment Health Res. 2024 Apr 2;3(1):12. doi: 10.1038/s44184-024-00056-z.

Abstract

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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4be8/10987499/461ba7a2a599/44184_2024_56_Fig1_HTML.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索