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Review
. 2025 Mar;34(1):e70007.
doi: 10.1002/mpr.70007.

From statistics to deep learning: Using large language models in psychiatric research

Yining Hua  1   2 , Andrew Beam  1   3 , Lori B Chibnik  1   4 , John Torous  2   5
Affiliations
Review

From statistics to deep learning: Using large language models in psychiatric research

Yining Hua et al. Int J Methods Psychiatr Res. 2025 Mar.

Abstract

Background: Large Language Models (LLMs) hold promise in enhancing psychiatric research efficiency. However, concerns related to bias, computational demands, data privacy, and the reliability of LLM-generated content pose challenges. GAP: Existing studies primarily focus on the clinical applications of LLMs, with limited exploration of their potentials in broader psychiatric research.

Objective: This study adopts a narrative review format to assess the utility of LLMs in psychiatric research, beyond clinical settings, focusing on their effectiveness in literature review, study design, subject selection, statistical modeling, and academic writing.

Implication: This study provides a clearer understanding of how LLMs can be effectively integrated in the psychiatric research process, offering guidance on mitigating the associated risks and maximizing their potential benefits. While LLMs hold promise for advancing psychiatric research, careful oversight, rigorous validation, and adherence to ethical standards are crucial to mitigating risks such as bias, data privacy concerns, and reliability issues, thereby ensuring their effective and responsible use in improving psychiatric research.

Keywords: artificial intelligence; clinical psychiatry; large language models; machine learning; psychiatric epidemiology; psychiatry.

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Conflict of interest statement

JT has research support from Otsuka and is an adviser to Precision Mental Wellness. All other authors have no conflict of interest.

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