Explainable Multimodal Depression Recognition in Clinical Interviews via PHQ-Aligned Symptom Summarization
Researchers have developed a new framework called Explain-MDRC for recognizing depression in clinical interviews. It combines text, audio, and facial cues to identify symptoms and provides interpretable results that clinicians can understand. The system uses structured summaries of patient responses, aligned with the PHQ-8 scale, which is commonly used to assess depression severity. This approach aims to improve reproducibility and clinician review by providing transparent AI
Researchers have developed a new framework called Explain-MDRC for recognizing depression in clinical interviews. It combines text, audio, and facial cues to identify symptoms and provides interpretable results that clinicians can understand. The system uses structured summaries of patient responses, aligned with the PHQ-8 scale, which is commonly used to assess depression severity. This approach aims to improve reproducibility and clinician review by providing transparent AI-assisted diagnosis.
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Why it matters: This matters because current AI systems for depression recognition often lack interpretability, making it difficult for clinicians to understand their decisions. Explain-MDRC addresses this issue, enabling more transparent and trustworthy AI-assisted diagnosis.
Source: https://arxiv.org/abs/2501.16106
This article was originally published at: https://arxiv.org/abs/2501.16106