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Computational and ethical considerations for using large language models in psychotherapy

Nature Computational Science volume 5, pages 854–862 (2025)Cite this article

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Abstract

Large language models (LLMs) hold great potential for augmenting psychotherapy by enhancing accessibility, personalization and engagement. However, a systematic understanding of the roles that LLMs can play in psychotherapy remains underexplored. In this Perspective, we propose a taxonomy of LLM roles in psychotherapy that delineates six specific roles of LLMs across two key dimensions: artificial intelligence autonomy and emotional engagement. We discuss key computational and ethical challenges, such as emotion recognition, memory retention, privacy and emotional dependency, and offer recommendations to address these challenges.

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Fig. 1: Framework of LLM roles in psychotherapy, categorized across two dimensions: autonomy and emotional engagement.

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Acknowledgements

Our research has been supported by the Ministry of Education, Singapore (A-8002610-00-00 (Y.-C.L.) and A-8000877-00-00 (R.Z.)) and an NUS Start-up Grant (A-8000529-00-00). This research work is partially supported by NUS CSSH Seed Fund Grant funding (CSSH-SF24-07 YCL).

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Authors and Affiliations

  1. Wee Kim Wee School of Communication and Information, Nanyang Technological University, Singapore, Singapore

    Renwen Zhang

  2. Department of Computer Science, National University of Singapore, Singapore, Singapore

    Han Meng & Yi-Chieh Lee

  3. SafeSpace, Singapore, Singapore

    Marion Neubronner

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  1. Renwen Zhang
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  2. Han Meng
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  3. Marion Neubronner
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  4. Yi-Chieh Lee
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Contributions

R.Z. conceptualized the study, developed the primary theoretical framework and drafted the paper. H.M. authored sections on computational challenges, and developed and designed the tables and figures. M.N. provided expertise in ensuring the clinical validity and practical applicability of the proposed approaches within mental health settings. Y.-C.L. contributed to the paper writing and made critical revisions for intellectual content. All authors reviewed, edited and approved the final paper.

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Correspondence to Yi-Chieh Lee.

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Nature Computational Science thanks Dan Adler, Nils Köbis and Ryan Louie for their contribution to the peer review of this work. Primary Handling Editor: Ananya Rastogi, in collaboration with the Nature Computational Science team.

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Zhang, R., Meng, H., Neubronner, M. et al. Computational and ethical considerations for using large language models in psychotherapy. Nat Comput Sci 5, 854–862 (2025). https://doi.org/10.1038/s43588-025-00874-x

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