CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning (2505.01199v2)

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-LLM that integrates a foundation audio model with the reasoning capabilities of LLMs, 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.

Overview of CaReAQA: An Integrated Diagnostic Audio-LLM

The paper entitled "CaReAQA: A Cardiac and Respiratory Audio Question Answering Model for Open-Ended Diagnostic Reasoning" introduces an innovative approach to medical diagnostics by integrating audio analysis with the capabilities of LLMs. Recognizing the challenges inherent in analyzing medical audio signals such as heart and lung sounds, the authors propose a model that overcomes the limitations of traditional methods that often rely on handcrafted features or supervised deep learning models, which demand extensive labeled data sets.

Key Contributions

CaReAQA stands out as a pioneering audio-LLM specifically tailored for medical diagnostics. By fusing an audio foundation model with LLMs, it provides clinicians with open-ended diagnostic responses based on cardiac and respiratory sound data. The incorporation of LLMs into this framework facilitates context-aware answers that adapt to the multiplicity of real-world medical scenarios.

In addition, the paper makes a significant contribution by introducing the CaReSound benchmark dataset consisting of annotated medical audio recordings enriched with metadata and paired question-answer examples. This dataset aims to propel further research in diagnostic reasoning within the medical field.

Experimental Results

The efficacy of CaReAQA is evidenced by its performance, achieving 86.2% accuracy on open-ended diagnostic reasoning tasks, surpassing baseline models. The model also demonstrates impressive generalization prowess by scoring an average accuracy of 56.9% on closed-ended classification tasks across unseen datasets. These results highlight the model's ability to interpret complex patterns in medical audio and deliver relevant diagnostic insights.

Implications and Future Directions

On a practical level, CaReAQA provides an AI-based framework that could enhance clinical decision support systems by delivering accurate and efficient diagnostic reasoning, potentially improving patient outcomes. Theoretically, the integration of LLMs with medical audio analysis sets a precedent for cross-modal innovations in AI, suggesting pathways for more complex diagnostic tools that could evolve from this foundation.

The authors propose several avenues for future work. These include expanding the dataset to capture broader demographic and clinical scenarios, improving multimodal fusion techniques to enhance interpretability, and navigating regulatory pathways to enable real-world deployment of such systems, which could ultimately transform the landscape of health monitoring and diagnostics.

Despite these advances, the authors emphasize CaReAQA's role not as a clinical deployment tool but as a step towards advancing auscultation-based diagnostic reasoning. Further development and validation are required to ensure its safe and effective application in clinical settings.

In sum, CaReAQA embodies a significant stride towards sophisticated AI systems capable of handling the intricacies of medical audio data and delivering nuanced diagnostic reasoning. The fusion of audio analysis with natural language responses elucidates new possibilities within AI-driven healthcare applications, providing promising insights for researchers in this domain.