ReviewInternational journal of methods in psychiatric research2025
From statistics to deep learning: Using large language models in psychiatric research.
Review in International journal of methods in psychiatric research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Interpretable depressive symptoms screening via statistical reasoning-augmented large language models using wearable and environmental data.Scientific reports · 2026Article
- Understanding psychiatrist readiness for AI: a study of access, self-efficacy, trust, and design expectations.BMC health services research · 2026Article
- Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis.Psychiatry research · 2026Article
- Role of large language models in mental health research: an international survey of researchers' practices and perspectives.BMJ mental health · 2025Article
- From statistics to deep learning: Using large language models in psychiatric research.International journal of methods in psychiatric research · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLarge 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.
objectiveThis 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.