ArticleNPP - digital psychiatry and neuroscience2024
Opportunities and Risks of Large Language Models in Psychiatry.
Article in NPP - digital psychiatry and neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Generative AI in Mental Health: Systematic Review of Capabilities and Limitations.JMIR mental health · 2025Pooled it
- Evaluating psychiatric conference posters: Benchmarking a custom generative pre-trained transformer against human inter-rater variability using a structured assessment framework.Indian journal of psychiatry · 2026Article
- Artificial Intelligence Applications in Mental Health: A Systematic Review of Clinical Practice, Educational Transformation, and Ethical Governance.Healthcare (Basel, Switzerland) · 2026Review
- Responsible and innovative AI for mental health care: five priority themes.NPP - digital psychiatry and neuroscience · 2026Review
- Real-world use of large language models for mental health in 2024.NPJ digital medicine · 2026Article
- Artificial Intelligence in Psychiatric Graduate Medical Education.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Scalable, context-sensitive psychiatric assessment with large language models and brief diaries.Psychological medicine · 2026Article
- Characterizing the spiral: potential mechanisms in AI-associated delusions.NPP - digital psychiatry and neuroscience · 2026Review
- Quality and readability of AI-generated information on bipolar disorder: a cross-sectional content analysis.BMC psychiatry · 2026Article
- Article
- Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review.Translational andrology and urology · 2026Review
- Safety Mechanisms and Risk Mitigation in Generative AI Mental Health Chatbots: A Systematic Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Exploring the potential of ChatGPT as a digital advisor in acute psychiatric crises: a feasibility study.Der Nervenarzt · 2026Article
- Help-Seeking in the Age of AI: Cross-Sectional Survey of the Use and Perceptions of AI-Based Mental Health Support Among US Adults.JMIR mental health · 2026Article
- Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety.Research square · 2026Article
- Prediction of 12-Week Remission in Patients With Depressive Disorder Using Reasoning-Based Large Language Models: Model Development and Validation Study.JMIR mental health · 2026Article
- Assessing the impact of safety guardrails on large language models using irritability metrics.NPJ digital medicine · 2026Article
- Conversational AI or a human professional for mental health advice? Exploring prevalence and public preferences in Australian adults.Australian journal of psychology · 2026Article
- Integrating expert knowledge into large language models improves performance for psychiatric reasoning and diagnosis.Psychiatry research · 2026Article
- A conceptual multi-agent architecture for mental health triage in post-conflict Arabic-speaking populations: a theoretical proposition and staged validation argument.Frontiers in psychiatry · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
The integration of Large Language Models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their potential to enhance mental healthcare through improved diagnostic accuracy, personalized care, and streamlined administrative processes. It is also acknowledged that LLMs introduce challenges related to computational demands, potential for misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure their safe deployment. We explore both the promise of LLMs in enriching psychiatric care and research through examples such as predictive analytics and therapy chatbots and risks including labor substitution, privacy concerns, and the necessity for responsible AI practices. We conclude by advocating for processes to develop responsible guardrails, including red teaming, multi-stakeholder oriented safety, and ethical guidelines/frameworks, to mitigate risks and harness the full potential of LLMs for advancing mental health.
Identifiers
What OpenQuestion 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 OpenQuestion graph.