ArticleNeuropsychopharmacology : official publication of the American College of Neuropsychopharmacology2024
Clinical decision support for bipolar depression using large language models.
Article in Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 2 of them syntheses that pooled it.
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Who cites it
30 citing papers in PubMed, 2 syntheses or guidelines pooled it, 51 citations in OpenAlex.
- The Application and Ethical Implication of Generative AI in Mental Health: Systematic Review.JMIR mental health · 2025Pooled it
- Large Language Models for Mental Health Applications: Systematic Review.JMIR mental health · 2024Pooled it
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- Large language models in respiratory care: safety and clinical integration.ERJ open research · 2026Article
- Large Language Models and Their Applications in Mental Health: Scoping Review.JMIR mental health · 2026Article
- Artificial Intelligence and the Potential Transformation of Mental Health.JAMA psychiatry · 2026Article
- Artificial Intelligence-Empowered Multimodal Learning in Psychiatry: A Scoping Review.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2026Review
- Artificial intelligence in psychiatry: clinical applications, limitations, and ethical challenges.Frontiers in behavioral neuroscience · 2026Article
- Mapping applications and evaluations of LLM-enabled AI chatbots for health purposes: a scoping review.Frontiers in public health · 2026Article
- Large Language Models for Cardiovascular Disease, Cancer, and Mental Disorders: A Review of Systematic Reviews.Healthcare (Basel, Switzerland) · 2025Review
- TCMEval-PA: a question-answering benchmark dataset for the prescription audit of Traditional Chinese Medicine.Scientific data · 2025Article
- Reporting guideline for the use of Generative Artificial intelligence tools in MEdical Research: the GAMER Statement.BMJ evidence-based medicine · 2025Article
- Clinical decision support for pharmacologic management of treatment-resistant depression with augmented large language models.Journal of mood and anxiety disorders · 2025Article
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- Using a fine-tuned large language model for symptom-based depression evaluation.NPJ digital medicine · 2025Article
- The Impact of Access to Clinical Guidelines on LLM-Based Treatment Recommendations for Chronic Hepatitis B.Liver international : official journal of the International Association for the Study of the Liver · 2025Article
- Optimizing large language models for detecting symptoms of depression/anxiety in chronic diseases patient communications.NPJ digital medicine · 2025Article
- A Multimodal Depression Consultation Dataset of Speech and Text with HAMD-17 Assessments.Scientific data · 2025Article
- Artificial Intelligence-Based Methods and Omics for Mental Illness Diagnosis: A Review.Bioengineering (Basel, Switzerland) · 2025Review
- Large Language Models' Clinical Decision-Making on When to Perform a Kidney Biopsy: Comparative Study.Journal of medical Internet research · 2025Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
Abstract
Management of depressive episodes in bipolar disorder remains challenging for clinicians despite the availability of treatment guidelines. In other contexts, large language models have yielded promising results for supporting clinical decisionmaking. We developed 50 sets of clinical vignettes reflecting bipolar depression and presented them to experts in bipolar disorder, who were asked to identify 5 optimal next-step pharmacotherapies and 5 poor or contraindicated choices. The same vignettes were then presented to a large language model (GPT4-turbo; gpt-4-1106-preview), with or without augmentation by prompting with recent bipolar treatment guidelines, and asked to identify the optimal next-step pharmacotherapy. Overlap between model output and gold standard was estimated. The augmented model prioritized the expert-designated optimal choice for 508/1000 vignettes (50.8%, 95% CI 47.7-53.9%; Cohen's kappa = 0.31, 95% CI 0.28-0.35). For 120 vignettes (12.0%), at least one model choice was among the poor or contraindicated treatments. Results were not meaningfully different when gender or race of the vignette was permuted to examine risk for bias. By comparison, an un-augmented model identified the optimal treatment for 234 (23.0%, 95% CI 20.8-26.0%; McNemar's p < 0.001 versus augmented model) of the vignettes. A sample of community clinicians scoring the same vignettes identified the optimal choice for 23.1% (95% CI 15.7-30.5%) of vignettes, on average; McNemar's p < 0.001 versus augmented model. Large language models prompted with evidence-based guidelines represent a promising, scalable strategy for clinical decision support. In addition to prospective studies of efficacy, strategies to avoid clinician overreliance on such models, and address the possibility of bias, will be needed.
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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.