Objective:
Voice is one of humanity’s most accessible yet underused windows into health. The Bridge2AI-Voice initiative, funded by the NIH Common Fund, is pioneering a national effort to unlock the diagnostic power of the human voice by building one of the largest, ethically sourced, and clinically validated voice datasets. By linking recordings with clinical, demographic, and behavioral data across five disease domains, Voice2AI aims to transform how clinicians detect, monitor, and understand disease, making early, equitable, and affordable voice-based screening a reality worldwide.
This momentum has sparked an unprecedented convergence of fields. The Bridge2AI-Voice panel will bring together leaders in speech-language pathology, laryngology, data science, and ethics to explore how collaborative science, ethical frameworks, and innovation are redefining what’s possible at the intersection of voice and AI. Panelists will share lessons learned, real-world use cases, and a vision for the future of voice as a biomarker of health.

Session Overview / Design:
This 50-minute session features a moderated discussion among four leaders representing the Bridge2AI-Voice ecosystem:

Program Leadership: Framing the vision for a sustainable, FAIR (Findable, Accessible, Interoperable, Reusable) data infrastructure and governance framework.

Speech-Language Pathology: Describing how clinical voice assessment standards ensure reliable, ethically collected datasets and inform future AI tools.

Data Science: Highlighting innovations in data curation, privacy, annotation, and bias mitigation that make complex voice data usable for research.

Laryngology: Demonstrating how AI-driven voice analysis is transforming diagnosis, treatment planning, and interdisciplinary collaboration for voice disorders and other disorders

Panelists will engage the audience with case examples, ethical dilemmas, and “what if” scenarios to foster dialogue on responsible and creative uses of AI in voice health.

Expected Outcomes:
Attendees will gain insight into how large-scale, ethically designed datasets empower clinical and research communities, strategies for integrating AI into voice workflows, and the collaborative potential to advance innovation while protecting patient trust.

Conclusions:
Bridge2AI-Voice embodies the next phase of translational voice science, where clinicians, researchers, and technologists work together to keep the human voice central in the age of artificial intelligence.