This Research Topic has been developed in collaboration with The Bridge2AI-Voice Consortium, which is part of the Bridge2AI Program, funded by the NIH Common Fund. You can read and download their 2025 annual symposium abstracts here and the annual symposium proceedings here.

While there have been many past studies at the intersection of voice data and machine learning, these efforts are often centered around binary classifiers built on simple acoustic features that do not generalize. As such, many voice AI methods have not been implemented in clinical environments. To realize the clinical potential of audio data, greater emphasis must be placed on methods that capture the inherent multimodality of audio data - including voice, speech, and language - and are designed for robustness and scalability from the outset. AI research studies should also be oriented around diverse, real-world datasets, including longitudinal records, and develop interpretable models that are flexible across different input structures.

This Research Topic seeks to bridge this critical gap. We aim to showcase research that not only develops novel AI and machine learning techniques for analyzing voice as a source of health information, but also places a strong emphasis on maximizing the potential for implementation, clinical validation, and actionable insights. Suitable themes for manuscripts include (but are not limited to):

- the intersection of generative AI and audio data for healthcare applications;
- the use of multimodal data, with a particular emphasis on voice, speech, and language data;
- the use of “realistic” data from real-world environments, with a particular emphasis on audio recordings collected from hospital environments;
- the application of AI models to longitudinal (“time-series”) voice records as a biomarker of health;
- clinical validation of voice biomarkers in large, diverse cohorts, demonstrating diagnostic/prognostic utility and generalizability;
- research practical aspects of integrating voice AI tools into existing healthcare infrastructures at scale, including EHR integration, workflow optimization, user acceptability (clinician and patient), and addressing barriers to adoption.

Are you interested in contributing a manuscript to this Research Topic?

Scalable Methods and Emerging Technologies to Advance Clinical Implementation of Vocal Biomarkers