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Large language models could change the future of behavioral healthcare: a proposal for responsible development and evaluation.
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.
Afiliação
  • Stade EC; Dissemination and Training Division, National Center for PTSD, VA Palo Alto Health Care System, Palo Alto, CA, USA. betsystade@stanford.edu.
  • Stirman SW; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA. betsystade@stanford.edu.
  • Ungar LH; Institute for Human-Centered Artificial Intelligence & Department of Psychology, Stanford University, Stanford, CA, USA. betsystade@stanford.edu.
  • Boland CL; Dissemination and Training Division, National Center for PTSD, VA Palo Alto Health Care System, Palo Alto, CA, USA.
  • Schwartz HA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
  • Yaden DB; Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.
  • Sedoc J; Dissemination and Training Division, National Center for PTSD, VA Palo Alto Health Care System, Palo Alto, CA, USA.
  • DeRubeis RJ; Department of Computer Science, Stony Brook University, Stony Brook, NY, USA.
  • Willer R; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
  • Eichstaedt JC; Department of Technology, Operations, and Statistics, New York University, New York, NY, USA.
Npj Ment Health Res ; 3(1): 12, 2024 Apr 02.
Article em En | MEDLINE | ID: mdl-38609507
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.

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article