RA-QA: A Benchmarking System for Respiratory Audio Question Answering Under Real-World Heterogeneity
arXiv:2602.18452v3 Announce Type: replace-cross Abstract: As conversational multimodal AI tools are increasingly adopted to process patient data for health assessment, robust benchmarks are needed to measure progress and expose failure modes under realistic conditions. Despite the importance of respiratory audio for mobile health screening, respiratory audio question answering remains underexplored, with existing studies evaluated narrowly and lacking real-world heterogeneity across modalities, devices, and question types. We hence introduce the \textbf{Respiratory-Audio Question-Answering (RA
The rapid adoption of multimodal AI in healthcare processing patient data necessitates robust benchmarks to ensure reliability and identify critical failure modes under realistic conditions.
This benchmark addresses a significant gap in evaluating AI's diagnostic capabilities for respiratory health, a critical area for mobile health screening, by introducing real-world heterogeneity.
The development of RA-QA allows for more rigorous and realistic evaluation of AI models designed for respiratory audio question answering, pushing towards more reliable and adaptable diagnostic tools.
- · AI healthcare developers
- · Medical diagnostic companies
- · Digital health platforms
- · Patients with respiratory conditions
- · AI models lacking robustness
- · Traditional diagnostic methods (long term)
- · Companies relying on narrow AI benchmarks
Improved accuracy and reliability of AI-driven respiratory diagnostics.
Accelerated integration of AI tools into mobile health screening and remote patient monitoring.
Potential for early and widespread detection of respiratory diseases, leading to better intervention and public health outcomes.
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Read at arXiv cs.LG