TY - JOUR
T1 - Master protocols in vocal biomarker development to reduce variability and advance clinical precision
T2 - a narrative review
AU - Kalia, Ayush
AU - Boyer, Micah
AU - Fagherazzi, Guy
AU - Bélisle-Pipon, Jean Christophe
AU - Bensoussan, Yael
AU - Bridge2AI-Voice Consortium
N1 - Funding:
The author(s) declare that financial support was received for the research and/or publication of this article. This work was supported by the National Institute for Health Grant #3OT2OD032720-01S2.
© 2025 Kalia, Boyer, Fagherazzi, Bélisle-Pipon and Bensoussan.
PY - 2025/6/27
Y1 - 2025/6/27
N2 - Introduction: Vocal biomarkers, defined as acoustic or linguistic features extracted from voice samples, are an emerging innovation in medical diagnostics. Utilizing artificial intelligence, machine learning, or traditional acoustic analysis, vocal biomarkers have shown promise in detecting and monitoring conditions such as respiratory disorders and cognitive impairments. Despite their potential, the lack of standardized protocols for data collection and analysis has limited their clinical applicability. Objectives: This review assesses the current state of research on developing a master protocol for vocal biomarkers, identifying key aspects essential for reducing variability across studies. It also explores insights from digital biomarker research to inform the creation of a standardized framework for vocal biomarker development. Methods: A narrative review was conducted by searching PubMed for literature on vocal and digital biomarker development. Articles were evaluated based on their proposed frameworks and recommendations for addressing methodological inconsistencies. Results: Twenty-one relevant articles were identified, including 12 focused on vocal biomarkers and 9 addressing broader digital biomarkers. Vocal biomarker literature emphasized the lack of existing master protocols and the need for standardization. In contrast, digital biomarker research from organizations like the Digital Medicine Society offered structured frameworks applicable to voice research. Conclusion: There is currently no established master protocol for vocal biomarker development. This review highlights foundational elements necessary for future standardization efforts to support the clinical integration of vocal biomarkers in healthcare.
AB - Introduction: Vocal biomarkers, defined as acoustic or linguistic features extracted from voice samples, are an emerging innovation in medical diagnostics. Utilizing artificial intelligence, machine learning, or traditional acoustic analysis, vocal biomarkers have shown promise in detecting and monitoring conditions such as respiratory disorders and cognitive impairments. Despite their potential, the lack of standardized protocols for data collection and analysis has limited their clinical applicability. Objectives: This review assesses the current state of research on developing a master protocol for vocal biomarkers, identifying key aspects essential for reducing variability across studies. It also explores insights from digital biomarker research to inform the creation of a standardized framework for vocal biomarker development. Methods: A narrative review was conducted by searching PubMed for literature on vocal and digital biomarker development. Articles were evaluated based on their proposed frameworks and recommendations for addressing methodological inconsistencies. Results: Twenty-one relevant articles were identified, including 12 focused on vocal biomarkers and 9 addressing broader digital biomarkers. Vocal biomarker literature emphasized the lack of existing master protocols and the need for standardization. In contrast, digital biomarker research from organizations like the Digital Medicine Society offered structured frameworks applicable to voice research. Conclusion: There is currently no established master protocol for vocal biomarker development. This review highlights foundational elements necessary for future standardization efforts to support the clinical integration of vocal biomarkers in healthcare.
KW - biomarkers
KW - digital biomarkers
KW - master protocol
KW - speech biomarkers
KW - vocal biomarkers
KW - voice
KW - voice AI
UR - https://www.scopus.com/pages/publications/105018049035
UR - https://pubmed.ncbi.nlm.nih.gov/40657648/
U2 - 10.3389/fdgth.2025.1619183
DO - 10.3389/fdgth.2025.1619183
M3 - Short survey
C2 - 40657648
AN - SCOPUS:105018049035
SN - 2673-253X
VL - 7
JO - Frontiers in Digital Health
JF - Frontiers in Digital Health
M1 - 1619183
ER -