MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models

• URL: http://arxiv.org/abs/2507.05591v1
• Date: Tue, 08 Jul 2025 01:56:39 GMT
• Title: MLlm-DR: Towards Explainable Depression Recognition with MultiModal Large Language Models
• Authors: Wei Zhang, Juan Chen, En Zhu, Wenhong Cheng, YunPeng Li, Yanbo J. Wang,
• Abstract summary: Automated depression diagnosis aims to analyze multimodal information from interview videos to predict participants' depression scores.<n>Previous studies often lack clear explanations of how these scores were determined, limiting their adoption in clinical practice.<n>We propose a novel multimodal large language model (MLlm-DR) that can understand multimodal information inputs and supports explainable depression diagnosis.
• Score: 28.873959594226605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated depression diagnosis aims to analyze multimodal information from interview videos to predict participants' depression scores. Previous studies often lack clear explanations of how these scores were determined, limiting their adoption in clinical practice. While the advent of LLMs provides a possible pathway for explainable depression diagnosis, current LLMs capable of processing multimodal data lack training on interview data, resulting in poor diagnostic performance when used directly. In this paper, we propose a novel multimodal large language model (MLlm-DR) that can understand multimodal information inputs and supports explainable depression diagnosis. MLlm-DR integrates a smaller LLMs and a lightweight query module (LQ-former). Specifically, the smaller LLMs is designed to generate depression scores and corresponding evaluation rationales. To enhance its logical reasoning for domain-specific tasks while maintaining practicality, we constructed a robust training dataset to fine-tune it. Meanwhile, the LQ-former captures depression-related features from speech and visual data, aiding the model's ability to process multimodal information, to achieve comprehensive depression diagnosis. Our approach achieves state-of-the-art results on two interview-based benchmark datasets, CMDC and E-DAIC-WOZ, demonstrating its effectiveness and superiority.

Related papers

• MAM: Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis via Role-Specialized Collaboration [57.98393950821579]
  We introduce the Modular Multi-Agent Framework for Multi-Modal Medical Diagnosis (MAM)<n>Inspired by our empirical findings, MAM decomposes the medical diagnostic process into specialized roles: a General Practitioner, Specialist Team, Radiologist, Medical Assistant, and Director.<n>This modular and collaborative framework enables efficient knowledge updates and leverages existing medical LLMs and knowledge bases.
  arXiv  Detail & Related papers  (2025-06-24T17:52:43Z)
• Truth in the Few: High-Value Data Selection for Efficient Multi-Modal Reasoning [71.3533541927459]
  We propose a novel data selection paradigm termed Activation Reasoning Potential (RAP)<n>RAP identifies cognitive samples by estimating each sample's potential to stimulate genuine multi-modal reasoning.<n>Our RAP method consistently achieves superior performance using only 9.3% of the training data, while reducing computational costs by over 43%.
  arXiv  Detail & Related papers  (2025-06-05T08:40:24Z)
• Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases [1.9662978733004604]
  Large language models (LLMs) have shown promising capabilities in transforming rare disease research.<n>This paper explores the integration of LLMs in the analysis of rare diseases, highlighting significant strides and pivotal studies.
  arXiv  Detail & Related papers  (2025-05-18T15:42:15Z)
• Generating Medically-Informed Explanations for Depression Detection using LLMs [1.325953054381901]
  Early detection of depression from social media data offers a valuable opportunity for timely intervention.<n>We propose LLM-MTD (Large Language Model for Multi-Task Depression Detection), a novel approach that combines the power of large language models with the crucial aspect of explainability.
  arXiv  Detail & Related papers  (2025-03-18T19:23:22Z)
• Structured Outputs Enable General-Purpose LLMs to be Medical Experts [50.02627258858336]
  Large language models (LLMs) often struggle with open-ended medical questions.<n>We propose a novel approach utilizing structured medical reasoning.<n>Our approach achieves the highest Factuality Score of 85.8, surpassing fine-tuned models.
  arXiv  Detail & Related papers  (2025-03-05T05:24:55Z)
• LlaMADRS: Prompting Large Language Models for Interview-Based Depression Assessment [75.44934940580112]
  This study introduces LlaMADRS, a novel framework leveraging open-source Large Language Models (LLMs) to automate depression severity assessment.<n>We employ a zero-shot prompting strategy with carefully designed cues to guide the model in interpreting and scoring transcribed clinical interviews.<n>Our approach, tested on 236 real-world interviews, demonstrates strong correlations with clinician assessments.
  arXiv  Detail & Related papers  (2025-01-07T08:49:04Z)
• Mitigating Hallucinations of Large Language Models in Medical Information Extraction via Contrastive Decoding [92.32881381717594]
  We introduce ALternate Contrastive Decoding (ALCD) to solve hallucination issues in medical information extraction tasks. ALCD demonstrates significant improvements in resolving hallucination issues compared to conventional decoding methods.
  arXiv  Detail & Related papers  (2024-10-21T07:19:19Z)
• Understanding the Role of LLMs in Multimodal Evaluation Benchmarks [77.59035801244278]
  This paper investigates the role of the Large Language Model (LLM) backbone in Multimodal Large Language Models (MLLMs) evaluation. Our study encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs. Key findings reveal that some benchmarks allow high performance even without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone.
  arXiv  Detail & Related papers  (2024-10-16T07:49:13Z)
• MedExQA: Medical Question Answering Benchmark with Multiple Explanations [2.2246416434538308]
  This paper introduces MedExQA, a novel benchmark in medical question-answering to evaluate large language models' (LLMs) understanding of medical knowledge through explanations. By constructing datasets across five distinct medical specialties, we address a major gap in current medical QA benchmarks. Our work highlights the importance of explainability in medical LLMs, proposes an effective methodology for evaluating models beyond classification accuracy, and sheds light on one specific domain, speech language pathology.
  arXiv  Detail & Related papers  (2024-06-10T14:47:04Z)
• Don't Ignore Dual Logic Ability of LLMs while Privatizing: A Data-Intensive Analysis in Medical Domain [19.46334739319516]
  We study how the dual logic ability of LLMs is affected during the privatization process in the medical domain. Our results indicate that incorporating general domain dual logic data into LLMs not only enhances LLMs' dual logic ability but also improves their accuracy.
  arXiv  Detail & Related papers  (2023-09-08T08:20:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.