Master protocols in vocal biomarker development to reduce variability and advance clinical precision: a narrative review.

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.