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

Area of Science:

• Psychiatric Research
• Artificial Intelligence
• Large Language Models

Background:

• Large Language Models (LLMs) offer potential to improve efficiency in psychiatric research.
• Challenges include bias, computational needs, data privacy, and reliability of LLM outputs.
• Current research largely focuses on clinical LLM applications, neglecting broader research potentials.

Purpose of the Study:

• To review the utility of LLMs in psychiatric research outside of clinical applications.
• To assess LLM effectiveness in literature review, study design, subject selection, statistical modeling, and academic writing.

Main Methods:

• This study employed a narrative review methodology.
• The review synthesized existing literature on LLM applications in psychiatric research.

Main Results:

• LLMs show promise in enhancing various stages of the research process, from literature review to academic writing.
• Effective integration requires addressing concerns regarding bias, data privacy, and output reliability.

Conclusions:

• LLMs can significantly advance psychiatric research when integrated thoughtfully.
• Mitigating risks through careful oversight, validation, and ethical adherence is crucial for responsible LLM use in this field.