Patient concerns about AI-based voice analysis in healthcare

Although AI-based voice analysis technology has potential utility for disease screening, diagnosis, clinical documentation, and safety monitoring, few studies have examined concerns that patients may have about the use of voice data for medical purposes. This lack of data is concerning because clinical applications of voice analysis technologies have the potential not only to improve healthcare outcomes, but also to significantly impact patients’ healthcare experiences. If patients’ concerns are not identified and addressed proactively, patients may harbor skepticism about uses of voice analysis technology or reject it altogether. We conducted 15 focus groups with patients recruited from a large academic medical center in the Upper Midwest, USA, to evaluate reactions to emerging clinical uses of voice analysis technologies. Interviews were transcribed and coded, and qualitative data analysis was used to identify major themes. Focus groups were attended by 107 participants. Thematic analysis revealed that patients had multiple concerns about voice analysis technologies, including concerns about (1) the privacy of voice data, (2) the value of voice analysis technologies, and (3) the disruption of healthcare experiences. Patients expressed significant concerns about the use of voice analysis technologies in healthcare. Leaders of healthcare institutions should anticipate these concerns and seek to implement voice analysis technologies in a manner that addresses patient fears about their use.

Authors

Institutions

Publication Details

Journal
BMC Digital Health
Published
2025-11-05
DOI
https://doi.org/10.1186/s44247-025-00221-0
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Patient concerns about AI-based voice analysis in healthcare

Austin M. Stroud, Richard R. Sharp, Joel E. Pacyna, J. Wise et al.
BMC Digital Health
Artificial Intelligence in Healthcare and Education
article

Patient concerns about AI-based voice analysis in healthcare

Austin M. Stroud, Richard R. Sharp, Joel E. Pacyna, J. Wise, Michele D. Anzabi
article en

Abstract

Although AI-based voice analysis technology has potential utility for disease screening, diagnosis, clinical documentation, and safety monitoring, few studies have examined concerns that patients may have about the use of voice data for medical purposes. This lack of data is concerning because clinical applications of voice analysis technologies have the potential not only to improve healthcare outcomes, but also to significantly impact patients’ healthcare experiences. If patients’ concerns are not identified and addressed proactively, patients may harbor skepticism about uses of voice analysis technology or reject it altogether. We conducted 15 focus groups with patients recruited from a large academic medical center in the Upper Midwest, USA, to evaluate reactions to emerging clinical uses of voice analysis technologies. Interviews were transcribed and coded, and qualitative data analysis was used to identify major themes. Focus groups were attended by 107 participants. Thematic analysis revealed that patients had multiple concerns about voice analysis technologies, including concerns about (1) the privacy of voice data, (2) the value of voice analysis technologies, and (3) the disruption of healthcare experiences. Patients expressed significant concerns about the use of voice analysis technologies in healthcare. Leaders of healthcare institutions should anticipate these concerns and seek to implement voice analysis technologies in a manner that addresses patient fears about their use.

BMC Digital HealthVol. 3(1)
Mayo Clinic (US), Mayo Clinic in Florida (US)
Openalex Percentile: Top 75%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Patient concerns about AI-based voice analysis in healthcare — Austin M. Stroud, Richard R. Sharp, et al. · BMC Digital Health (2025) | TGRS Research Map | TGRS