Stade, E. C., et al. (2024).
Npj Mental Health Research, 3(1).
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.
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This article examines the potential of large language models (LLMs), such as GPT-4 and Google’s Gemini, to support and transform behavioral healthcare, particularly psychotherapy. LLMs could enhance access to care by automating administrative tasks like documentation and session summaries, assisting with treatment planning, and supporting clinician training. The authors propose a phased integration of LLMs, starting with low-risk assistive roles, moving toward collaborative functions with human oversight, and potentially, though more controversially, fully autonomous psychotherapy.
While LLMs offer promising opportunities to improve efficiency and scale mental health services, the authors emphasize the need for cautious, evidence-based development due to significant ethical, safety, and accountability concerns. They call for ongoing collaboration between clinicians, researchers, and technologists to ensure LLM use in mental healthcare prioritizes patient safety, transparency, and effectiveness through rigorous testing and gradual implementation.