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

• Published in: International journal of methods in psychiatric research Vol. 34; no. 1; pp. e70007 - n/a
• Main Authors: Hua, Yining, Beam, Andrew, Chibnik, Lori B., Torous, John
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.03.2025; John Wiley and Sons Inc; Wiley
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biomedical Research - methods; Biomedical Research - standards; clinical psychiatry; Deep Learning; Humans; Language; Large language models; Literature reviews; Long Didactic Paper; machine learning; Mathematical functions; Mental health; Models, Statistical; Natural language processing; Neural networks; Privacy; psychiatric epidemiology; Psychiatric research; psychiatry; Psychiatry - methods; Research methodology; Researchers; Statistical models; clinical psychiatry; large language models; psychiatric epidemiology; psychiatry; machine learning; artificial intelligence
• ISSN: 1049-8931; 1557-0657; 1557-0657
• DOI: 10.1002/mpr.70007

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.