Bridging AI innovation and healthcare: scalable clinical validation methods for voice biomarkers - Krautz, Agnieszka Ewa; Langner, Jörg; Helmhold, Florian; Volkening, Julia; Hoffmann, Alina; Hasler, Claudio (Frontiers in Digital Health, 2025)
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, large-scale 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.