Large AI models trained on audio data may have the potential to rapidly
classify patients, enhancing medical decision-making and potentially improving
outcomes through early detection. Existing technologies depend on limited
datasets using expensive recording equipment in high-income, English-speaking
countries. This challenges deployment in resource-constrained, high-volume
settings where audio data may have a profound impact. 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. This application
ultimately results in an audio electronic health record (voice EHR) which may
contain complex biomarkers of health from conventional voice/respiratory
features, speech patterns, and language with semantic meaning - compensating
for the typical limitations of unimodal clinical datasets. This report
introduces a consortium of partners for global work, presents the application
used for data collection, and showcases the potential of informative voice EHR
to advance the scalability and diversity of audio AI.

通过使用移动 / 网络应用程序进行指导性问题，捕捉健康数据并形成音频电子健康记录（Voice EHR），可以利用大型 AI 模型对患者进行快速分类，提升医疗决策，并通过早期检测潜在地改善结果。该报告引入了全球工作的合作伙伴群体、数据收集所使用的应用程序，并展示了信息丰富的声音电子健康记录（Voice EHR）通过弥补单模临床数据集的典型限制，从常规语音 / 呼吸特征、语音模式和具有语义含义的语言中获得的复杂生物标识可能带来的潜在可扩展性和多样性。