AUTHOR=Bahr Ruth , Anibal James , Bedrick Steven , Bélisle-Pipon Jean-Christophe , Bensoussan Yael , Blaylock Nate , Castermans Joris , Comito Keith , Dorr David , Hale Greg , Jackson Christie , Krussel Andrea , Kuman Kimberly , Komarlu Akash Raj , Lerner-Ellis Jordan , Powell Maria , Ravitsky Vardit , Rameau Anaïs , Reavis Charlie , Sigaras Alexandros , Cruz Samantha Salvi , Vojtech Jenny , Urbano Megan , Watts Stephanie , Zhao Robin , Toghranegar Jamie ,  the Bridge2AI-Voice Consortium , Bensoussan Yael , Elemento Olivier , Rameau Anaïs , Sigaras Alexandros , Ghosh Satrajit , Powell Maria , Ravitsky Vardit , Belisle-Pipon Jean Christophe , Dorr David , Payne Phillip , Johnson Alistair , Bahr Ruth , Bolser Donald , Rudzicz Frank , Ellis Jordan Lerner , Jenkins Kathy , Awan Shaheen , Boyer Micah , Hersh Bill , Krussel Andrea , Bedrick Steven , Syed Toufeeq Ahmed , Toghranegar Jamie , Anibal James , Sutherland Duncan , Diaz-Ocampo Enrique , Silberhoz Elizabeth , Costello John , Gelbard Alexander , Vinson Kimberly , Neal Tempestt , Jayachandran Lochana , Ng Evan , Casalino Selina , Abdel-Aty Yassmeen , Hanna Karim , Zesiewicz Theresa , Moothedan Elijah , Evangelista Emily , Cruz Samantha Salvi , Zhao Robin , Ebraheem Mohamed , Newberry Karlee , De Santiago Iris , Eiseman Ellie , Rahman JM , Jo Stacy , Goldenberg Anna 

TITLE=Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium

JOURNAL=Frontiers in Digital Health

VOLUME=Volume 6 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1484818

DOI=10.3389/fdgth.2024.1484818

ISSN=2673-253X

ABSTRACT=IntroductionThe 2024 Voice AI Symposium, presented by the Bridge2AI-Voice Consortium, featured deep-dive educational workshops conducted by experts from diverse fields to explore the latest advancements in voice biomarkers and artificial intelligence (AI) applications in healthcare. Through five workshops, attendees learned about topics including international standardization of vocal biomarker data, real-world deployment of AI solutions, assistive technologies for voice disorders, best practices for voice data collection, and deep learning applications in voice analysis. These workshops aimed to foster collaboration between academia, industry, and healthcare to advance the development and implementation of voice-based AI tools.MethodsEach workshop featured a combination of lectures, case studies, and interactive discussions. Transcripts of audio recordings were generated using Whisper (Version 7.13.1) and summarized by ChatGPT (Version 4.0), then reviewed by the authors. The workshops covered various methodologies, from signal processing and machine learning operations (MLOps) to ethical concerns surrounding AI-powered voice data collection. Practical demonstrations of AI-driven tools for voice disorder management and technical discussions on implementing voice AI models in clinical and non-clinical settings provided attendees with hands-on experience.ResultsKey outcomes included the discussion of international standards to unify stakeholders in vocal biomarker research, practical challenges in deploying AI solutions outside the laboratory, review of Bridge2AI-Voice data collection processes, and the potential of AI to empower individuals with voice disorders. Additionally, presenters shared innovations in ethical AI practices, scalable machine learning frameworks, and advanced data collection techniques using diverse voice datasets. The symposium highlighted the successful integration of AI in detecting and analyzing voice signals for various health applications, with significant advancements in standardization, privacy, and clinical validation processes.DiscussionThe symposium underscored the importance of interdisciplinary collaboration to address the technical, ethical, and clinical challenges in the field of voice biomarkers. While AI models have shown promise in analyzing voice data, challenges such as data variability, security, and scalability remain. Future efforts must focus on refining data collection standards, advancing ethical AI practices, and ensuring diverse dataset inclusion to improve model robustness. By fostering collaboration among researchers, clinicians, and technologists, the symposium laid a foundation for future innovations in AI-driven voice analysis for healthcare diagnostics and treatment.