PERSPECTIVE article
Front. Digit. Health
Sec. Connected Health
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1575753
This article is part of the Research TopicAdvancing Vocal Biomarkers and Voice AI in Healthcare: Multidisciplinary Focus on Responsible and Effective Development and UseView all 8 articles
Bridging AI Innovation and Healthcare: Scalable Clinical Validation Methods for Voice Biomarkers
Provisionally accepted
- PeakProfiling, Berlin, Germany
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The integration of artificial intelligence (AI) in voice biomarker analysis presents a transformative opportunity for objective and non-invasive diagnostics in healthcare. However, clinical adoption remains limited due to challenges such as data scarcity, model generalizability, and regulatory hurdles. This perspective article explores effective and scalable methods for clinical validation of voice biomarkers, emphasizing the importance of proprietary technology, high-quality, diverse datasets, strong clinical partnerships, and regulatory compliance. We propose a multifaceted approach leveraging proprietary AI technology (Musicology AI) to enhance voice analysis, largescale data collection initiatives to improve model robustness, and medical device certification to ensure clinical applicability. Addressing technical, ethical, and regulatory challenges is crucial for establishing trust in AI-driven diagnostics. By combining technological innovation with rigorous clinical validation, this work aims to bridge the gap between research and real-world implementation, paving the way for AI-powered voice biomarkers to become a reliable tool in digital healthcare.
Keywords: Voice biomarkers, artificial intelligence, clinical validation, Healthcare implementation, Mental Health
Received: 12 Feb 2025; Accepted: 12 Jun 2025.
Copyright: © 2025 Krautz, Langner, Helmhold, Volkening, Hoffmann and Hasler. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Agniesza Ewa Krautz, PeakProfiling, Berlin, Germany
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.