The epistemic economy of vocal biomarker startups: a qualitative interview study of data practices and governance.

Background and aims: Vocal biomarker technology has emerged as a promising non-invasive health assessment tool, with artificial intelligence enabling the screening, diagnosis, and monitoring of conditions including Parkinson's disease, Alzheimer's disease, depression, and heart failure. Vocal biomarkers are defined as vocal features, alone or in combination, validated as indicators of clinical outcomes. Startups in this space are generating substantial investment and anticipatory discourse around future commercialization, while the scientific foundation and governance framework remain unestablished. Drawing on Sabina Leonelli's epistemic economy framework, which analyzes how data are produced, valued, and governed within social, institutional, and commercial arrangements, we examine how voice data are converted into entities with clinical and commercial value, and at what scientific and ethical cost. Methods: We performed a qualitative interview study using an interpretive framework. Virtual semi-structured interviews were conducted with representatives from eleven vocal biomarker startups identified through purposive sampling. Interviews addressed data collection methods, IRB compliance, informed consent, participant demographics, geographic sourcing, and financial incentives. Two researchers independently performed thematic analysis, reconciling differences via discussion. Results: Six themes were identified: (1) the absence of shared data standards introduced substantial hardware and protocol heterogeneity, uncontrolled confounding, and label noise, undermining scientific validity; (2) IRB engagement ranged from robust multi-site review to complete absence of oversight; (3) consent practices varied from granular opt-in to implied consent and minimal disclosures; (4) voice data were acknowledged as inherently non-anonymizable, creating unresolved privacy and re-identification risks; (5) data sourcing was asymmetric, with collection from low- and middle-income countries in 4 out of 11 startups while targeting commercial deployment in high-income countries; and (6) commercial incentives produced proprietary protocols that preclude independent validation and impede data sharing. Conclusion: The vocal biomarker startup ecosystem operates as an epistemic economy in which methodological, regulatory, and governance choices tend to favor commercial interests and high-income populations over scientific rigor and equity. Standardized data provenance requirements, reassessment of voice data's risk classification under existing privacy frameworks (e.g., HIPAA, GDPR), and participatory governance including source communities are urgently needed to ensure vocal biomarker products are scientifically valid, ethically grounded, and equitably developed.