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

arXiv: https://arxiv.org/abs/2505.01199 · HTML: https://mlsanity.com/paper/2505.01199/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 2 May 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 14 of 14)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language Models](https://mlsanity.com/paper/2311.07919/) (2023)
- [AudioGPT: Understanding and Generating Speech, Music, Sound, and Talking Head](https://mlsanity.com/paper/2304.12995/) (2023)
- [Pengi: An Audio Language Model for Audio Tasks](https://mlsanity.com/paper/2305.11834/) (2023)
- [Listen, Think, and Understand](https://mlsanity.com/paper/2305.10790/) (2023)
- [GAMA: A Large Audio-Language Model with Advanced Audio Understanding and Complex Reasoning Abilities](https://mlsanity.com/paper/2406.11768/) (2024)
- [Open-Ended Medical Visual Question Answering Through Prefix Tuning of Language Models](https://mlsanity.com/paper/2303.05977/) (2023)
- [The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification](https://mlsanity.com/paper/2108.00813/) (2021)
- [Towards Open Respiratory Acoustic Foundation Models: Pretraining and Benchmarking](https://mlsanity.com/paper/2406.16148/) (2024)
- [BUET Multi-disease Heart Sound Dataset: A Comprehensive Auscultation Dataset for Developing Computer-Aided Diagnostic Systems](https://mlsanity.com/paper/2409.00724/) (2024)

## Built on by (most cited first, 8 of 8)

- [MedMosaic: A Challenging Large Scale Benchmark of Diverse Medical Audio](https://mlsanity.com/paper/2605.00969/) (2026)
- [StethoLM: Audio Language Model for Cardiopulmonary Analysis Across Clinical Tasks](https://mlsanity.com/paper/2603.00355/) (2026)
- [AURA Score: A Metric For Holistic Audio Question Answering Evaluation](https://mlsanity.com/paper/2510.04934/) (2025)
- [RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity](https://mlsanity.com/paper/2602.18452/) (2026)
- [RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering](https://mlsanity.com/paper/2603.06542/) (2026)
- [Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings](https://mlsanity.com/paper/2603.13362/) (2026)
- [Adaptive Test-Time Scaling for Zero-Shot Respiratory Audio Classification](https://mlsanity.com/paper/2604.12647/) (2026)
- [Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio](https://mlsanity.com/paper/2606.23243/) (2026)

## Abstract (arXiv)

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.