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Workshop summaries from the 2024 voice AI symposium, presented by the Bridge2AI-voice consortium
Ruth Bahr1, James Anibal2,3, Steven Bedrick4
1Department of Communication Sciences & Disorders, University of South Florida, Tampa, FL, United States.
Frontiers in Digital Health
|November 14, 2024
Summary
The 2024 Voice AI Symposium advanced voice biomarkers and artificial intelligence (AI) in healthcare. Experts discussed standardizing vocal data, deploying AI solutions, and ethical AI practices for improved diagnostics and treatment.
Area of Science:
- Healthcare Technology
- Artificial Intelligence
- Biomedical Engineering
Background:
- The 2024 Voice AI Symposium convened experts to discuss advancements in voice biomarkers and AI applications.
- Workshops covered international standardization, real-world AI deployment, assistive technologies, data collection, and deep learning in voice analysis.
Purpose of the Study:
- To foster collaboration between academia, industry, and healthcare.
- To advance the development and implementation of voice-based AI tools in healthcare.
- To explore the latest innovations in voice biomarkers and AI.
Main Methods:
- Five educational workshops featuring lectures, case studies, and interactive discussions.
- Audio recording transcripts generated using Whisper and summarized by ChatGPT.
- Exploration of methodologies including signal processing, machine learning operations (MLOps), and ethical considerations.
Main Results:
- Discussion on international standards for vocal biomarker research and practical deployment challenges.
- Review of data collection processes and the potential of AI for voice disorder management.
- Highlighting advancements in ethical AI, scalable machine learning, and diverse voice datasets for health applications.
Conclusions:
- Interdisciplinary collaboration is crucial for addressing technical, ethical, and clinical challenges in voice biomarkers.
- AI shows promise in voice data analysis, but data variability, security, and scalability require further attention.
- Future efforts should focus on refining data standards, ethical practices, and diverse datasets to enhance AI model robustness in healthcare.
Keywords:
Bridge2AIBridge2AI-Voiceartificial intelligenceartificial intelligence—AIaudiomicsethical AIvoice biomarkervoice biomarkersMore Related Videos
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