Voice EHR: Introducing Multimodal Audio Data for Health
This addresses health equity issues by enabling audio data collection in high-volume, low-resource environments, though it is incremental as it builds on existing audio AI concepts with a new data collection method.
The paper tackles the challenge of deploying AI models for clinical tasks in resource-constrained settings by introducing a mobile/web app that collects multimodal audio health data (Voice EHR) through guided questions, with initial experiments showing potential to enhance scalability and diversity of audio AI.
Artificial intelligence (AI) models trained on audio data may have the potential to rapidly perform clinical tasks, enhancing medical decision-making and potentially improving outcomes through early detection. Existing technologies depend on limited datasets collected with expensive recording equipment in high-income countries, which challenges deployment in resource-constrained, high-volume settings where audio data may have a profound impact on health equity. This report introduces a novel data type and a corresponding collection system that captures health data through guided questions using only a mobile/web application. The app facilitates the collection of an audio electronic health record (Voice EHR) which may contain complex biomarkers of health from conventional voice/respiratory features, speech patterns, and spoken language with semantic meaning and longitudinal context, potentially compensating for the typical limitations of unimodal clinical datasets. This report presents the application used for data collection, initial experiments on data quality, and case studies which demonstrate the potential of voice EHR to advance the scalability/diversity of audio AI.