CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning
Journal:
arXiv
Published Date:
May 2, 2025
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.
