ArticleNPJ digital medicine2025
A scoping review of large language models for generative tasks in mental health care.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers, 2 of them syntheses 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
61 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Generative AI in Youth Mental Health Apps: Rapid Review.JMIR mental health · 2026Pooled it
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Harnessing large language models in virtual CBT for university students' academic anxiety: a preliminary randomized trial.Frontiers in public health · 2026Trial
- Possible Role and Function of AI Conversational Agents in Dialectical Behavior Therapy for Borderline Personality Disorder: Qualitative Interview Study.JMIR mental health · 2026Article
- LLM-Based Psychiatric Interview Simulation: Technical Development and Pilot Expert-Based Content Validation of a Voice Prototype.Academic psychiatry : the journal of the American Association of Directors of Psychiatric Residency Training and the Association for Academic Psychiatry · 2026Article
- Frameworks, Methodologies, and Tools for Evaluating Large Language Models in Digital Mental Health Interventions: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Large Language Models for Distress Rating in Korean Psycho-Oncology Interviews: Exploratory Clinician-Benchmarked Evaluation Study.Journal of medical Internet research · 2026Article
- Large Language Model-Based Behavioral Activation Chatbot for Young People With Depression Using Artificial Users and Clinical Experts: Mixed Methods Evaluation.JMIR mental health · 2026Article
- General-Purpose Artificial Intelligence Use in Routine Mental Health Practice Among Australian Clinicians: Mixed Methods Study.Journal of medical Internet research · 2026Article
- Generative Large Language Models in Mental Health Care Settings: Systematic Review and Meta-Analysis.JMIR AI · 2026Review
- A scoping review on the mental health harms of LLM-based chatbots.NPJ digital medicine · 2026Article
- The Reliability of Human Evaluation of Large Language Models in Health Care Settings: Scoping Review.Journal of medical Internet research · 2026Article
- Multiagent Large Language Model Framework for Psychotherapy Fidelity Assessment in Motivational Interviewing and Cognitive Behavioral Therapy Training: Cross-Sectional, Simulation-Based Evaluation Study.JMIR medical education · 2026Article
- Artificial Intelligence in Psychiatry: Five Decades of Progress and Persistent Translational Challenges.Psychiatric research and clinical practice · 2026Article
- Should I and Can I Use AI for Forensic Psychiatry Report Writing?JMIR mental health · 2026Article
- Can Large Language Models Support University Counseling? Evidence from Perceived Counseling Alliance, Disclosure Willingness, and Risk Recognition.Behavioral sciences (Basel, Switzerland) · 2026Article
- Generative AI Chatbots Could Take Over Our Emotional Lives: The Potential and Challenges of Generative AI for Health Globally.Clinical psychology in Europe · 2026Article
- AI-Guided Cognitive Behavioral Therapy for Depression and Anxiety: Bridging the Mental Health Treatment Gap Through Digital Psychiatry.Healthcare (Basel, Switzerland) · 2026Review
- Contextualizing AI-Supported Emotion Regulation Through Sport and Exercise in Higher Education: A Conceptual Reframing.Behavioral sciences (Basel, Switzerland) · 2026Article
- Natural Language Processing Applied to Psychiatric Clinical Notes: Scoping Review.JMIR medical informatics · 2026Article
1 more citing papers are in PubMed but not listed here.
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
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This scoping review synthesizes existing research on LLM applications in mental health care, reviews model performance and clinical effectiveness, identifies gaps in current evaluation methods following a structured evaluation framework, and provides recommendations for future development. A systematic search identified 726 unique articles, of which 16 met the inclusion criteria. These studies, encompassing applications such as clinical assistance, counseling, therapy, and emotional support, show initial promises. However, the evaluation methods were often non-standardized, with most studies relying on ad-hoc scales that limit comparability and robustness. A reliance on prompt-tuning proprietary models, such as OpenAI's GPT series, also raises concerns about transparency and reproducibility. As current evidence does not fully support their use as standalone interventions, more rigorous development and evaluation guidelines are needed for safe, effective clinical integration.
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.