Evidence map›Paper›PMID 39055787›Full record

ArticleDigital health

Stakeholder perspectives on ethical and trustworthy voice AI in health care.

Jean-Christophe Bélisle-Pipon, Maria Powell, Renee English, Marie-Françoise Malo, Vardit Ravitsky, Bridge2AI–Voice Consortium, Yael Bensoussan

Abstract read
In one paragraph

Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Ethics dumping in artificial intelligence.Frontiers in artificial intelligence · 2024
    Article
  13. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Jean-Christophe Bélisle-PiponFaculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.ORCID https://orcid.org/0000-0002-8965-8153
Maria PowellVanderbilt University Medical Center, Department of Otolaryngology-Head & Neck Surgery, Nashville, TN, Canada.
Renee EnglishFaculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
Marie-Françoise MaloFaculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada.
Vardit RavitskyHastings Center, Garrison, NY, USA.
Bridge2AI–Voice Consortium
Yael BensoussanDepartment of Otolaryngology-Head & Neck Surgery, University of South Florida, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: Voice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation. Methods: A questionnaire was administered to voice AI experts, clinicians, scholars, patients, trainees, and policy-makers who participated at the 2023 Voice AI Symposium organized by the Bridge2AI-Voice AI Consortium. The survey used a mix of Likert scale, ranking and open-ended questions. A total of 27 stakeholders participated in the study. Results: The main results of the study are the identification of priorities in terms of ethical issues, an initial definition of ethically sourced data for voice AI, insights into the use of synthetic voice data, and proposals for acting on the trustworthiness of voice AI. The study shows a diversity of perspectives and adds nuance to the planning and development of ethical and trustworthy voice AI. Conclusions: This study represents the first stakeholder survey related to voice as a biomarker of health published to date. This study sheds light on the critical importance of ethics and trustworthiness in the development of voice AI technologies for health applications.

Indexed as

AI in medicinebiomarkersdata collectionethical considerationshealth datahealth disparitiesinclusivitytelehealthtrustworthinessvoice AI

Identifiers

PMID39055787
PMCPMC11271113

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.