CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning

• URL: http://arxiv.org/abs/2505.01199v2
• Date: Sun, 01 Jun 2025 09:47:18 GMT
• Title: CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
• Authors: Tsai-Ning Wang, Lin-Lin Chen, Neil Zeghidour, Aaqib Saeed,
• Abstract summary: CaReAQA is an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models.<n>We introduce CaReSound, a benchmark dataset of annotated medical audio recordings enriched with metadata.<n> evaluation results show that CaReAQA achieves 86.2% accuracy on open-ended diagnostic reasoning tasks.
• Score: 17.462121203082006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical audio signals, such as heart and lung sounds, play a crucial role in clinical diagnosis. However, analyzing these signals remains challenging: traditional methods rely on handcrafted features or supervised deep learning models that demand extensive labeled datasets, limiting their scalability and applicability. To address these issues, we propose CaReAQA, an audio-language model that integrates a foundation audio model with the reasoning capabilities of large language models, enabling clinically relevant, open-ended diagnostic responses. Alongside CaReAQA, we introduce CaReSound, a benchmark dataset of annotated medical audio recordings enriched with metadata and paired question-answer examples, intended to drive progress in diagnostic reasoning research. Evaluation results show that CaReAQA achieves 86.2% accuracy on open-ended diagnostic reasoning tasks, outperforming baseline models. It also generalizes well to closed-ended classification tasks, achieving an average accuracy of 56.9% on unseen datasets. Our findings show how audio-language integration and reasoning advances medical diagnostics, enabling efficient AI systems for clinical decision support.

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