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Open Access

Large Language Models in Psychiatry: Current Applications, Limitations, and Future Scope

Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, and School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, China
Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
School of Information Science and Technology, Institute of Computational Biology, Northeast Normal University, Changchun 130024, China

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Abstract

With the advancements in Artificial Intelligence (AI) technology, Large Language Models (LLMs) provide outstanding capabilities for natural language understanding and generation, enhancing various domains. In psychiatry, LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy, enhance therapeutic communication, and personalize patient care with their strength in understanding and generating human-like text. In clinical AI, developing and utilizing robust and interpretable models has been a longstanding challenge. This survey investigates the current psychiatric practice of LLMs, along with a series of corpus resources that could be used for training psychiatric LLMs. We discuss the limitations concerning LLM reproducibility, capabilities, usability, interpretability in clinical settings, and ethical considerations. Additionally, we propose potential future directions for research, clinical application, and education in psychiatric LLMs. Finally, we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

Keywords

Artificial Intelligence (AI)Large Language Model (LLM)psychiatrymedical application

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Big Data Mining and Analytics
Volume 7 Issue 4,
December 2024
Pages 1148-1168
DOI: 10.26599/BDMA.2024.9020046
Cite this article:
Liu Z, Bao Y, Zeng S, et al. Large Language Models in Psychiatry: Current Applications, Limitations, and Future Scope. Big Data Mining and Analytics, 2024, 7(4): 1148-1168. https://doi.org/10.26599/BDMA.2024.9020046

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Received: 20 February 2024
Revised: 30 May 2024
Accepted: 07 July 2024
Published: 04 December 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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