ReviewThe Lancet. Digital health2025
Large language models for the mental health community: framework for translating code to care.
Review in The Lancet. Digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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
23 citing papers in PubMed.
- Cerina-Cognitive Behavioral Therapy-Based Mobile App for Managing Generalized Anxiety Disorder Symptoms Among University Students: Results From a Pilot Feasibility Randomized Controlled Trial.JMIR mHealth and uHealth · 2025Trial
- AI Chatbot Use and Disclosure for Mental Health Among US Adolescents and Young Adults.JAMA pediatrics · 2026Article
- Inferring Personality From Social Media Activity Using Large Language Models: Cross-Model Agreement, Temporal Stability, and Convergent Validity With Self-Reports.Journal of personality · 2026Article
- Behavior Change Content and Implementation of Large Language Model-Driven Conversational Agents in Cardiometabolic Care: Scoping Review.Journal of medical Internet research · 2026Article
- Large language models for post-discharge follow-up in erectile dysfunction care: a narrative review.Translational andrology and urology · 2026Review
- The doctor is not in, but the Chatbot is: Utah's experience regulating mental health AI.NPJ digital medicine · 2026Article
- Interpretable depression assessment using a large language model.PLOS digital health · 2026Article
- Leveraging Large Language Models for Early Detection of Anomaly Work Injury Cases: Data-Driven Approach to Rehabilitation Efficiency.JMIR rehabilitation and assistive technologies · 2026Article
- A guided chatbot-based psychological intervention for psychologically distressed older adolescents and young adults: a randomised clinical trial in Jordan.NPJ digital medicine · 2026Article
- Clinical reasoning with machines: evaluating the interpretive depth of AI in urological case assessments.BMC urology · 2026Observational
- A roadmap for medical large language models: a review of foundations, applications, and challenges.Military Medical Research · 2026Review
- Generative AI for pre-consultation mental health triage in disorders of gut-brain interaction.Frontiers in psychiatry · 2026Article
- Artificial intelligence in college students' mental health education: opportunities, challenges, and strategic responses.Frontiers in psychology · 2026Review
- AI-assisted assessment of the IFSO consensus on obesity management medications in the context of metabolic bariatric surgery.PLOS digital health · 2025Article
- Clinical Obesity Through the Lens of Context-Aware Large Language Models.Obesity surgery · 2025Article
- Accelerating Digital Mental Health: The Society of Digital Psychiatry's Three-Pronged Road Map for Education, Digital Navigators, and AI.JMIR mental health · 2025Article
- Large language models in clinical psychiatry: Applications and optimization strategies.World journal of psychiatry · 2025Review
- Potential of ChatGPT in youth mental health emergency triage: Comparative analysis with clinicians.PCN reports : psychiatry and clinical neurosciences · 2025Article
- Exploring the potential of lightweight large language models for AI-based mental health counselling task: a novel comparative study.Scientific reports · 2025Article
- The future of the sleep field using large language models in mental health care.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
Large language models (LLMs) offer promising applications in mental health care to address gaps in treatment and research. By leveraging clinical notes and transcripts as data, LLMs could improve diagnostics, monitoring, prevention, and treatment of mental health conditions. However, several challenges persist, including technical costs, literacy gaps, risk of biases, and inequalities in data representation. In this Viewpoint, we propose a sociocultural-technical approach to address these challenges. We highlight five key areas for development: (1) building a global clinical repository to support LLMs training and testing, (2) designing ethical usage settings, (3) refining diagnostic categories, (4) integrating cultural considerations during development and deployment, and (5) promoting digital inclusivity to ensure equitable access. We emphasise the need for developing representative datasets, interpretable clinical decision support systems, and new roles such as digital navigators. Only through collaborative efforts across all stakeholders, unified by a sociocultural-technical framework, can we clinically deploy LLMs while ensuring equitable access and mitigating risks.
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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 Socratic graph.