RAMoEA-QA: Hierarchical Specialization for Robust Respiratory Audio Question Answering
Abstract
Conversational generative AI is rapidly entering healthcare, where general-purpose models must integrate heterogeneous patient signals and support diverse interaction styles while producing clinically meaningful outputs. In respiratory care, non-invasive audio, such as recordings captured via mobile microphones, enables scalable screening and longitudinal monitoring, but the heterogeneity challenge is particularly acute: recordings vary widely across devices, environments, and acquisition protocols, and questions span multiple intents and question formats. Existing biomedical audio-language QA systems are typically monolithic, without any specialization mechanisms for tackling diverse respiratory corpora and query intents. They are also only validated in limited settings, leaving it unclear how reliably they handle the shifts encountered in real-world settings. To address these limitations, we introduce RAMoEA-QA, a hierarchically routed generative model for respiratory audio question answering that unifies multiple question types and supports both discrete and continuous targets within a single multimodal system. RAMoEA-QA applies two-stage conditional specialization: an Audio Mixture-of-Experts routes each recording to a suitable pre-trained audio encoder, and a Language Mixture-of-Adapters selects a LoRA adapter on a shared frozen LLM to match the query intent and answer format. By specializing both acoustic representations and generation behaviour per example, RAMoEA-QA consistently outperforms strong baselines and routing ablations with minimal parameter overhead, improving in-domain test accuracy to 0.72 (vs. 0.61 and 0.67 for state-of-the-art baselines) and exhibiting the strongest generalization for diagnosis under domain, modality, and task shifts.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of RAMoEA-QA, a hierarchically specialized model for respiratory audio question answering that effectively addresses the challenges posed by heterogeneous audio data and diverse query intents. This work represents a meaningful advancement in the integration of machine learning and healthcare, demonstrating both innovative methodology and impactful results.
Methodology
The proposed RAMoEA-QA model introduces a novel hierarchical specialization approach that employs a two-stage conditional specialization mechanism, utilizing an Audio Mixture-of-Experts and a Language Mixture-of-Adapters. This design allows the model to effectively handle the diverse nature of respiratory audio data and various query intents, which is a significant advancement over existing monolithic biomedical audio-language QA systems. The use of pre-trained audio encoders and LoRA adapters on a frozen LLM demonstrates a thoughtful integration of state-of-the-art techniques while maintaining a low parameter overhead.
Results & evaluation
The paper presents a comprehensive experimental setup, comparing RAMoEA-QA against strong baselines and conducting ablation studies to validate the effectiveness of the routing mechanisms. The reported in-domain test accuracy of 0.72 significantly surpasses the state-of-the-art baselines (0.61 and 0.67), indicating robust performance. The experiments also address generalization across different domains, modalities, and tasks, which is critical for real-world applications in healthcare.
Reproducibility
The authors provide a link to their code repository, which is essential for reproducibility. However, the paper could benefit from additional details regarding the implementation specifics, such as hyperparameter settings and training procedures, to facilitate easier replication of results by other researchers.
Limitations
One limitation noted is the reliance on the RA-QA collection, which may not encompass the full diversity of respiratory audio data encountered in practice. Additionally, while the model shows strong performance in controlled settings, its robustness in highly variable real-world environments remains to be fully validated.
Broader impact
The RAMoEA-QA model has significant potential applications in healthcare, particularly in respiratory care, where it can enhance patient monitoring and screening through scalable audio analysis. Its ability to handle diverse audio inputs and question formats could lead to more effective and personalized patient interactions, ultimately improving healthcare outcomes. The main contribution of this paper is the introduction of RAMoEA-QA, a hierarchically specialized model for respiratory audio question answering that effectively addresses the challenges posed by heterogeneous audio data and diverse query intents. This work represents a meaningful advancement in the integration of machine learning and healthcare, demonstrating both innovative methodology and impactful results.
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