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.
@article{arxiv.2404.01620,
title = {Voice EHR: Introducing Multimodal Audio Data for Health},
author = {James Anibal and Hannah Huth and Ming Li and Lindsey Hazen and Veronica Daoud and Dominique Ebedes and Yen Minh Lam and Hang Nguyen and Phuc Hong and Michael Kleinman and Shelley Ost and Christopher Jackson and Laura Sprabery and Cheran Elangovan and Balaji Krishnaiah and Lee Akst and Ioan Lina and Iqbal Elyazar and Lenny Ekwati and Stefan Jansen and Richard Nduwayezu and Charisse Garcia and Jeffrey Plum and Jacqueline Brenner and Miranda Song and Emily Ricotta and David Clifton and C. Louise Thwaites and Yael Bensoussan and Bradford Wood},
journal= {arXiv preprint arXiv:2404.01620},
year = {2024}
}