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arXiv · 2603.13362

Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings

Fan Wu·Tsai-Ning Wang·Nicolas Zumarraga·Ning Wang·Markus Kreft·Kevin O'Sullivan·Elgar Fleisch·Oliver Aalami·Paul Schmiedmayer·Robert Jakob·Patrick Langer
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Abstract

Auscultation is a vital diagnostic tool, yet its utility is often limited by subjective interpretation. While general-purpose Audio-Language Models (ALMs) excel in general domains, they struggle with the nuances of physiological signals. We propose a framework that aligns multi-site auscultation recordings directly with a frozen Large Language Model (LLM) embedding space via gated cross-attention. By leveraging the LLM's latent world knowledge, our approach moves beyond isolated classification toward holistic, patient-level assessment. On the CaReSound benchmark, our model achieves a state-of-the-art 0.865 F1-macro and 0.952 BERTScore. We demonstrate that lightweight, domain-specific encoders rival large-scale ALMs and that multi-site aggregation provides spatial redundancy that mitigates temporal truncation. This alignment of medical acoustics with text foundations offers a scalable path for bridging signal processing and clinical assessment.

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Fan Wu, Tsai-Ning Wang, Nicolas Zumarraga, Ning Wang, Markus Kreft, Kevin O'Sullivan, Elgar Fleisch, Oliver Aalami, Paul Schmiedmayer, Robert Jakob, Patrick Langer. 2026-03-09. Patient-Level Multimodal Question Answering from Multi-Site Auscultation Recordings. https://arxiv.org/abs/2603.13362

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