Frontiers in Digital Health (Oct 2024)

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

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

DOI
https://doi.org/10.3389/fdgth.2024.1484818
Journal volume & issue
Vol. 6

Abstract

Read online

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.

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