mlsanityData to 14 Oct 2026

PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children

arXiv 2412.14769 ↗First posted 19 Dec 2024cs.CLcited by 4

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Abstract (arXiv)

Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-risk LBCs is crucial, yet challenging due to the severe shortage of mental health professionals, especially in rural areas. While the House-Tree-Person (HTP) test shows higher child participation rates, its requirement for expert interpretation limits its application in resource-scarce regions. To address this challenge, we propose PsyDraw, a multi-agent system based on Multimodal Large Language Models that assists mental health professionals in analyzing HTP drawings. The system employs specialized agents for feature extraction and psychological interpretation, operating in two stages: comprehensive feature analysis and professional report generation. Evaluation of HTP drawings from 290 primary school students reveals that 71.03% of the analyzes achieved High Consistency with professional evaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The system identified 31.03% of cases requiring professional attention, demonstrating its effectiveness as a preliminary screening tool. Currently deployed in pilot schools, method shows promise in supporting mental health professionals, particularly in resource-limited areas, while maintaining high professional standards in psychological assessment.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 2 of its 21 references that are in the map. First posted on arXiv 19 Dec 2024.

Builds on

2 of 21 references.

References · 2

  1. 2023Visual Instruction Tuningcited by 7,869
  2. 2023Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyondcited by 2,224

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Cited by year

Citing papers per year: 2025: 4.

4 papers.

  1. 2025From LLM Reasoning to Autonomous AI Agents: A Comprehensive Reviewcited by 149
  2. 2025A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasurescited by 57
  3. 2025Value-Based Large Language Model Agent Simulation for Mutual Evaluation of Trust and Interpersonal Closenesscited by 3
  4. 2025Resource-Limited Joint Multimodal Sentiment Reasoning and Classification via Chain-of-Thought Enhancement and Distillationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025From Visual Perception to Deep Empathy: An Automated Assessment Framework for House-Tree-Person Drawings Using Multimodal LLMs and Multi-Agent Collaboration
  2. 2025Reasoning Like Experts: Leveraging Multimodal Large Language Models for Drawing-based Psychoanalysis
  3. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  4. 2025When and How to Integrate Multimodal Large Language Models in College Psychotherapy: Perspectives from Multi-stakeholders
  5. 2026A Multi-Agent Audit Framework for High-Stakes Reasoning: Evaluation and Interpretability in Clinical Mental Health Screening
  6. 2026Assessing Risks of Large Language Models in Mental Health Support: A Framework for Automated Clinical AI Red Teaming
  7. 2025From Explainability to Action: A Generative Operational Framework for Integrating XAI in Clinical Mental Health Screening
  8. 2026LingxiDiagBench: A Multi-Agent Framework for Benchmarking LLMs in Chinese Psychiatric Consultation and Diagnosis