{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T06:04:52Z","timestamp":1789452292102,"version":"build-2803163510"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100015326","name":"Common Fund","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100015326","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Background and aims<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1895383","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:36:41Z","timestamp":1789450601000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["The epistemic economy of vocal biomarker startups: a qualitative interview study of data practices and governance"],"prefix":"10.3389","volume":"8","author":[{"given":"Abigail","family":"Yuhan","sequence":"first","affiliation":[{"name":"Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College","place":["New York, NY, United States"]},{"name":"SUNY Downstate College of Medicine","place":["Brooklyn, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonal","family":"Swain","sequence":"additional","affiliation":[{"name":"Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robin","family":"Zhao","sequence":"additional","affiliation":[{"name":"Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John","family":"Ramos","sequence":"additional","affiliation":[{"name":"Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ana\u00efs","family":"Rameau","sequence":"additional","affiliation":[{"name":"Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medical College","place":["New York, NY, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"11687","DOI":"10.1038\/s41598-025-96575-6","article-title":"Explainable artificial intelligence to diagnose early Parkinson\u2019s disease via voice analysis","volume":"15","author":"Shen","year":"2025","journal-title":"Sci Rep"},{"key":"B2","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1370\/afm.240091","article-title":"Evaluation of an AI-based voice biomarker tool to detect signals consistent with moderate to severe depression","volume":"23","author":"Mazur","year":"2025","journal-title":"Ann Fam Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"3424","DOI":"10.3390\/s25113424","article-title":"The human voice as a digital health solution leveraging artificial intelligence","volume":"25","author":"Muddaloor","year":"2025","journal-title":"Sensors (Basel, Switzerland)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1353","DOI":"10.1016\/j.mayocp.2023.03.007","article-title":"Guess what we can hear\u2014novel voice biomarkers for the remote detection of disease","volume":"98","author":"Sara","year":"2023","journal-title":"Mayo Clin Proc"},{"key":"B5","doi-asserted-by":"publisher","first-page":"340","DOI":"10.1002\/ohn.830","article-title":"Voice as a biomarker in health-tech: mapping the evolving landscape of voice biomarkers in the start-up world","volume":"171","author":"Evangelista","year":"2024","journal-title":"Otolaryngol Head Neck Surg"},{"key":"B6","author":"O\u2019Connell","year":"2025"},{"key":"B7","year":"2026"},{"key":"B8","year":"2016"},{"key":"B9","doi-asserted-by":"publisher","first-page":"998","DOI":"10.1093\/heapol\/czaf054","article-title":"How to (or how not to)\u2026enhance equity in the conduct of global health research: dimensions and directions for organizations","volume":"40","author":"Nambiar","year":"2025","journal-title":"Health Policy Plan"},{"key":"B10","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1353\/hpu.2019.0119","article-title":"Breaking the cycle of health inequities: the bioethics of data","volume":"30","author":"Tossas-Milligan","year":"2019","journal-title":"J Health Care Poor Underserved"},{"key":"B11","volume-title":"How Data Cross Borders: Globalising Plant Knowledge Through Transnational Data Management and Its Epistemic Economy","author":"Leonelli","year":"2022"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1568159","DOI":"10.3389\/fdgth.2025.1568159","article-title":"\u201cVoice is the new blood\u201d: a discourse analysis of voice AI health-tech start-up websites","volume":"7","author":"Blatter","year":"2025","journal-title":"Front Digit Health"},{"key":"B13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10728-025-00539-w","article-title":"\u201cLow risk, high happiness\u201d: a review of openly declared ethical and legal practices in voice biomarker health-tech start-ups","author":"Gallois","year":"2025","journal-title":"Health Care Anal"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1484521","DOI":"10.3389\/fdgth.2025.1484521","article-title":"Interactive panel summaries of the 2024 voice AI symposium","volume":"7","author":"B\u00e9lisle-Pipon","year":"2025","journal-title":"Front Digit Health"},{"key":"B15","year":"2020"},{"key":"B16","volume-title":"MAXQDA 2024 [Computer Software]","year":"2024"},{"key":"B17","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1145\/3236386.3241340","article-title":"The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery","volume":"16","author":"Lipton","year":"2018","journal-title":"Queue"},{"key":"B18","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1613\/jair.606","article-title":"Identifying mislabeled training data","volume":"11","author":"Brodley","year":"1999","journal-title":"J. Artif. Int. Res"},{"key":"B19","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/s10462-004-0751-8","article-title":"Class noise vs. attribute noise: a quantitative study","volume":"22","author":"Zhu","year":"2003","journal-title":"Artif Intell Rev"},{"key":"B20","doi-asserted-by":"publisher","first-page":"e0000651","DOI":"10.1371\/journal.pdig.0000651","article-title":"Bias in medical AI: implications for clinical decision-making","volume":"3","author":"Cross","year":"2024","journal-title":"PLOS Digit Health"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1333","DOI":"10.1002\/lary.31052","article-title":"Current practices in voice data collection and limitations to voice AI research: a national survey","volume":"134","author":"Evangelista","year":"2024","journal-title":"Laryngoscope"},{"key":"B22","doi-asserted-by":"publisher","first-page":"675","DOI":"10.1111\/jlme.12311","article-title":"Biobanks, data sharing, and the drive for a global privacy governance framework. The journal of law, medicine & ethics: a journal of the American society of law","volume":"43","author":"Dove","year":"2015","journal-title":"Med Ethics"},{"key":"B23","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1159\/000510820","article-title":"Evaluation of speech-based digital biomarkers: review and recommendations","volume":"4","author":"Robin","year":"2020","journal-title":"Digit Biomark"},{"key":"B24","volume-title":"Responsible Research: A Systems Approach to Protecting Research Participants","author":"Federman","year":"2002"},{"key":"B25","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1159\/000515346","article-title":"Voice for health: the use of vocal biomarkers from research to clinical practice","volume":"5","author":"Fagherazzi","year":"2021","journal-title":"Digit Biomark"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e014131","DOI":"10.1136\/bmjgh-2023-014131","article-title":"Colonialism in the new digital health agenda","volume":"9","author":"Sekalala","year":"2024","journal-title":"BMJ Glob Health"},{"key":"B27","first-page":"855","article-title":"Reclaiming the digital commons: a public data trust for training data","author":"Chan","year":"2023"},{"key":"B28","doi-asserted-by":"publisher","first-page":"103576","DOI":"10.1016\/j.envsci.2023.103576","article-title":"From inequalities to epistemic innovation: insights from open science hardware projects in Latin America","author":"Arancio","year":"2023","journal-title":"Environ Sci Policy"},{"key":"B29","volume-title":"The Social Control of Technology","author":"Collingridge","year":"1980"},{"key":"B30","doi-asserted-by":"publisher","first-page":"20552076241260407","DOI":"10.1177\/20552076241260407","article-title":"Stakeholder perspectives on ethical and trustworthy voice AI in health care","volume":"10","author":"B\u00e9lisle-Pipon","year":"2024","journal-title":"Digit Health"},{"key":"B31","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.shpsa.2025.05.005","article-title":"Resisting epistemic exploitation: from institutional remedies to community-based research","volume":"111","author":"Rolin","year":"2025","journal-title":"Stud Hist Philos Sci"},{"key":"B32","first-page":"1433","article-title":"Knowledge-Enhanced language models are not bias-proof: situated knowledge and epistemic injustice in AI","author":"Kraft","year":"2024"},{"key":"B33","doi-asserted-by":"crossref","DOI":"10.1093\/acprof:oso\/9780199929023.001.0001","volume-title":"The Epistemology of Resistance: Gender and Racial Oppression, Epistemic Injustice, and the Social Imagination","author":"Medina","year":"2013"},{"key":"B34","author":"Pizzimenti","year":"2025"},{"key":"B35","doi-asserted-by":"publisher","first-page":"1750111","DOI":"10.3389\/fdgth.2026.1750111","article-title":"Using voice and speech data in healthcare: a scoping review of the ethical, legal and social implications","volume":"8","author":"Malo","year":"2026","journal-title":"Front Digit Health"},{"key":"B36","doi-asserted-by":"publisher","first-page":"e537","DOI":"10.1016\/S2589-7500(21)00141-2","article-title":"COVID-19 detection from audio: seven grains of salt","volume":"3","author":"Coppock","year":"2021","journal-title":"Lancet Digit Health"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1895383\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:36:42Z","timestamp":1789450602000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1895383\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":36,"alternative-id":["10.3389\/fdgth.2026.1895383"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1895383","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]},"article-number":"1895383"}}