# Large Language Models in Mental Health Care: a Scoping Review

arXiv: https://arxiv.org/abs/2401.02984 · HTML: https://mlsanity.com/paper/2401.02984/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 142 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 30 of 70)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Mistral 7B](https://mlsanity.com/paper/2310.06825/) (2023)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://mlsanity.com/paper/2107.13586/) (2021)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [BARTScore: Evaluating Generated Text as Text Generation](https://mlsanity.com/paper/2106.11520/) (2021)
- [Recipes for building an open-domain chatbot](https://mlsanity.com/paper/2004.13637/) (2020)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [GPTScore: Evaluate as You Desire](https://mlsanity.com/paper/2302.04166/) (2023)
- [Towards Emotional Support Dialog Systems](https://mlsanity.com/paper/2106.01144/) (2021)
- [UL2: Unifying Language Learning Paradigms](https://mlsanity.com/paper/2205.05131/) (2022)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation](https://mlsanity.com/paper/2104.12369/) (2021)
- [A Survey of Large Language Models in Medicine: Progress, Application, and Challenge](https://mlsanity.com/paper/2311.05112/) (2023)
- [PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental Health Support](https://mlsanity.com/paper/2106.01702/) (2021)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [A Primer on Neural Network Models for Natural Language Processing](https://mlsanity.com/paper/1510.00726/) (2015)

## Built on by (most cited first, 30 of 34)

- [Depression Detection on Social Media with Large Language Models](https://mlsanity.com/paper/2403.10750/) (2024)
- [Citrus: Leveraging Expert Cognitive Pathways in a Medical Language Model for Advanced Medical Decision Support](https://mlsanity.com/paper/2502.18274/) (2025)
- [Large Language Model for Mental Health: A Systematic Review](https://mlsanity.com/paper/2403.15401/) (2024)
- [Aligning Large Language Models for Enhancing Psychiatric Interviews Through Symptom Delineation and Summarization: Pilot Study](https://mlsanity.com/paper/2403.17428/) (2024)
- [A Comprehensive Evaluation of Large Language Models on Mental Illnesses](https://mlsanity.com/paper/2409.15687/) (2024)
- [Hybrid Training Approaches for LLMs: Leveraging Real and Synthetic Data to Enhance Model Performance in Domain-Specific Applications](https://mlsanity.com/paper/2410.09168/) (2024)
- [Applying and Evaluating Large Language Models in Mental Health Care: A Scoping Review of Human-Assessed Generative Tasks](https://mlsanity.com/paper/2408.11288/) (2024)
- [HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered Therapy Using LLM Agents](https://mlsanity.com/paper/2502.05982/) (2025)
- [Survey and Experiments on Mental Disorder Detection via Social Media: From Large Language Models and RAG to Agents](https://mlsanity.com/paper/2504.02800/) (2025)
- [ProMind-LLM: Proactive Mental Health Care via Causal Reasoning with Sensor Data](https://mlsanity.com/paper/2505.14038/) (2025)
- [AI shares emotion with humans across languages and cultures](https://mlsanity.com/paper/2506.13978/) (2025)
- [Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection](https://mlsanity.com/paper/2410.15623/) (2024)
- [Labeling Free-text Data using Language Model Ensembles](https://mlsanity.com/paper/2501.08413/) (2025)
- [CATCH: A Novel Data Synthesis Framework for High Therapy Fidelity and Memory-Driven Planning Chain of Thought in AI Counseling](https://mlsanity.com/paper/2509.25733/) (2025)
- [Evolution of meta's llama models and parameter-efficient fine-tuning of large language models: a survey](https://mlsanity.com/paper/2510.12178/) (2025)
- [AI Psychosis: Does Conversational AI Amplify Delusion-Related Language?](https://mlsanity.com/paper/2603.19574/) (2026)
- [Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Models](https://mlsanity.com/paper/2409.14794/) (2024)
- [Beyond Simulations: What 20,000 Real Conversations Reveal About Mental Health AI Safety](https://mlsanity.com/paper/2601.17003/) (2026)
- [Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMs](https://mlsanity.com/paper/2410.05684/) (2024)
- [Interaction Protocol Shapes Moral Judgment in Multi-Agent Debate](https://mlsanity.com/paper/2510.10002/) (2025)
- [Natural Language Tools: A Natural Language Approach to Tool Calling In Large Language Agents](https://mlsanity.com/paper/2510.14453/) (2025)
- [Small Agent Group is the Future of Digital Health](https://mlsanity.com/paper/2602.08013/) (2026)
- [Optimizing Psychological Counseling with Instruction-Tuned Large Language Models](https://mlsanity.com/paper/2406.13617/) (2024)
- [A Multi-Agent Dual Dialogue System to Support Mental Health Care Providers](https://mlsanity.com/paper/2411.18429/) (2024)
- [OnRL-RAG: Real-Time Personalized Mental Health Dialogue System](https://mlsanity.com/paper/2504.02894/) (2025)
- [AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewing](https://mlsanity.com/paper/2505.17380/) (2025)
- [From Reddit to Generative AI: Evaluating Large Language Models for Anxiety Support Fine-tuned on Social Media Data](https://mlsanity.com/paper/2505.18464/) (2025)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders](https://mlsanity.com/paper/2502.00229/) (2025)
- [Artificial Empathy: AI based Mental Health](https://mlsanity.com/paper/2506.00081/) (2025)

## Abstract (arXiv)

Objectieve:This review aims to deliver a comprehensive analysis of Large Language Models (LLMs) utilization in mental health care, evaluating their effectiveness, identifying challenges, and exploring their potential for future application. Materials and Methods: A systematic search was performed across multiple databases including PubMed, Web of Science, Google Scholar, arXiv, medRxiv, and PsyArXiv in November 2023. The review includes all types of original research, regardless of peer-review status, published or disseminated between October 1, 2019, and December 2, 2023. Studies were included without language restrictions if they employed LLMs developed after T5 and directly investigated research questions within mental health care settings. Results: Out of an initial 313 articles, 34 were selected based on their relevance to LLMs applications in mental health care and the rigor of their reported outcomes. The review identified various LLMs applications in mental health care, including diagnostics, therapy, and enhancing patient engagement. Key challenges highlighted were related to data availability and reliability, the nuanced handling of mental states, and effective evaluation methods. While LLMs showed promise in improving accuracy and accessibility, significant gaps in clinical applicability and ethical considerations were noted. Conclusion: LLMs hold substantial promise for enhancing mental health care. For their full potential to be realized, emphasis must be placed on developing robust datasets, development and evaluation frameworks, ethical guidelines, and interdisciplinary collaborations to address current limitations.