mlsanityData to 7 Oct 2026

Large Language Model for Mental Health: A Systematic Review

arXiv 2403.15401 ↗First posted 19 Feb 2024cs.CY cs.AI cs.CLcited by 14

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Abstract (arXiv)

Large language models (LLMs) have attracted significant attention for potential applications in digital health, while their application in mental health is subject to ongoing debate. This systematic review aims to evaluate the usage of LLMs in mental health, focusing on their strengths and limitations in early screening, digital interventions, and clinical applications. Adhering to PRISMA guidelines, we searched PubMed, IEEE Xplore, Scopus, JMIR, and ACM using keywords: 'mental health OR mental illness OR mental disorder OR psychiatry' AND 'large language models'. We included articles published between January 1, 2017, and April 30, 2024, excluding non-English articles. 30 articles were evaluated, which included research on mental health conditions and suicidal ideation detection through text (n=15), usage of LLMs for mental health conversational agents (CAs) (n=7), and other applications and evaluations of LLMs in mental health (n=18). LLMs exhibit substantial effectiveness in detecting mental health issues and providing accessible, de-stigmatized eHealth services. However, the current risks associated with the clinical use might surpass their benefits. The study identifies several significant issues: the lack of multilingual datasets annotated by experts, concerns about the accuracy and reliability of the content generated, challenges in interpretability due to the 'black box' nature of LLMs, and persistent ethical dilemmas. These include the lack of a clear ethical framework, concerns about data privacy, and the potential for over-reliance on LLMs by both therapists and patients, which could compromise traditional medical practice. Despite these issues, the rapid development of LLMs underscores their potential as new clinical aids, emphasizing the need for continued research and development in this area.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 35 of its 184 references that are in the map. First posted on arXiv 19 Feb 2024.

Builds on

35 of 184 references.

References · 35

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23 more
  1. 2021Towards Emotional Support Dialog Systemscited by 294
  2. 2020ARBERT & MARBERT: Deep Bidirectional Transformers for Arabiccited by 164
  3. 2023Self-contradictory Hallucinations of Large Language Models: Evaluation, Detection and Mitigationcited by 123
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 6; 2025: 7; 2026: 1.

14 papers.

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  1. 2024Building Trust in Mental Health Chatbots: Safety Metrics and LLM-Based Evaluation Toolscited by 9
  2. 2024Advancing Depression Detection on Social Media Platforms Through Fine-Tuned Large Language Modelscited by 4
  3. 2024Copiloting Diagnosis of Autism in Real Clinical Scenarios via LLMscited by 3
  4. 2025Pet-Bench: Benchmarking the Abilities of Large Language Models as E-Pets in Social Network Servicescited by 3
  5. 2025H2HTalk: Evaluating Large Language Models as Emotional Companioncited by 3
  6. 2024Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health carecited by 1
  7. 2024The use of GPT-4o and Other Large Language Models for the Improvement and Design of Self-Assessment Scales for Measurement of Interpersonal Communication Skillscited by 1
  8. 2025AI-Augmented LLMs Achieve Therapist-Level Responses in Motivational Interviewingcited by 1
  9. 2025A Confidence-Diversity Framework for Calibrating AI Judgement in Accessible Qualitative Coding Taskscited by 1
  10. 2024Understanding Student Sentiment on Mental Health Support in Colleges Using Large Language Modelscited by 0
  11. 2025When LLM Therapists Become Salespeople: Evaluating Large Language Models for Ethical Motivational Interviewingcited by 0
  12. 2025Artificial Empathy: AI based Mental Healthcited by 0
2 more
  1. 2025Context-Emotion Aware Therapeutic Dialogue Generation: A Multi-component Reinforcement Learning Approach to Language Models for Mental Health Supportcited by 0
  2. 2026Generative Confidants: How do People Experience Trust in Emotional Support from Generative AI?cited by 0