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

Большие языковые модели могут изменить будущее поведенческого здравоохранения: предложение по ответственному развитию и оценке
David B. Yaden, Robb Willer, Shannon Wiltsey Stirman, Robert J. DeRubeis, Lyle Ungar, H. Andrew Schwartz, Johannes C. Eichstaedt, Elizabeth Cameron Stade, Cody L. Boland, João Sedoc
2024-04-02

clinical LLMslarge language modelspsychotherapy automationresponsible development and evaluationrisk detection and bias in clinical AI
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.
1
Clinical psychology is a high-stakes domain requiring nuanced expertise, so integrating LLMs into psychotherapy poses significant risks that must be addressed.
2
LLMs could enable large-scale studies of evidence-based interventions, potentially challenging existing assumptions about psychotherapy.
3
LLMs like GPT-4 and Gemini have strong potential to support, augment, or eventually automate aspects of psychotherapy, increasing access and scalability of personalized treatments.
4
Recommendations for responsible development include centering clinical science, interdisciplinary collaboration, and attention to assessment, risk detection, transparency, and bias.
5
The paper proposes a staged roadmap for responsible integration of LLMs into psychotherapy, drawing parallels to autonomous vehicle development.

Clinical large language models for psychotherapy (LLMs applied to clinical/therapeutic settings)

Responsible development, integration, evaluation, and risk management of LLMs in psychotherapy, including assessment, risk detection, transparency, bias, clinical-science centering, and interdisciplinary collaboration

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Publication Date
2024-04-02
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Authors
David B. Yaden
Robb Willer
Shannon Wiltsey Stirman
Robert J. DeRubeis
Lyle Ungar
H. Andrew Schwartz
Johannes C. Eichstaedt
Elizabeth Cameron Stade
Cody L. Boland
João Sedoc
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