Understanding AI Acceptance in Healthcare and 'Glue Ear' Diagnosis: A Survey of Clinicians and Service Users.
6 affiliations
- 1Department of Applied Psychology, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff CF5 2YB, UK.
- 2Centre for Speech and Language Therapy and Hearing Science, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Cardiff CF5 2YB, UK.
- 3Public Involvement Consultancy, Cardiff CF5 2YB, UK.
- 4ENT Department, University Hospital of Wales, Cardiff & Vale University Health Board, Cardiff CF5 2YB, UK.
- 5The First College of Clinical Medicine, Xuzhou Medical University, Xuzhou 221000, China.
- 6Department of Otolaryngology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou 221000, China.
Abstract
Background: This pilot survey explored the views of clinicians and service users regarding the use of AI in a specific diagnostic scenario of OME. Methods: An online survey was conducted in collaboration with a Patient and Public Involvement and Engagement panel and advertised through research and clinical networks, social media platforms. Participants (N = 95; 21-72 years) included clinicians (59%) and service users (32%; patients and carers) with experience of OME. Quantitative and qualitative data were collected and analysed, comparing the views of the two groups across the two contexts. Results: Participants demonstrated a generally positive view of AI in OME diagnostics and in healthcare. Clinicians and service users reported comparable levels of trust and acceptance, although clinicians expressed greater concerns about flexibility and accuracy. Perceived key benefits included time and cost savings, while transparency and trust appeared as pivotal facilitators for acceptance. Frequent concerns centred on operational reliability and ethical considerations, with less emphasis on data confidentiality. Conclusions: The findings indicate broad support for AI applications both in OME diagnosis and more widely in healthcare, contingent on addressing concerns about accuracy, human oversight, and ethical use. Recommendations include focused communication strategies emphasising transparency and human oversight in the clinical decision-making processes.
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BibTeX
@article{bennett2026understanding,
title = {{Understanding AI Acceptance in Healthcare and 'Glue Ear' Diagnosis: A Survey of Clinicians and Service Users.}},
author = {Alexis Bennett and Fei Zhao and Henry Yeomans and Andrew Hall and Wen Jiang},
journal = {Audiology research},
year = {2026},
month = oct,
doi = {10.3390/audiolres16050157},
url = {https://europepmc.org/article/MED/42930355},
note = {CC BY},
}
RIS
TY - JOUR
TI - Understanding AI Acceptance in Healthcare and 'Glue Ear' Diagnosis: A Survey of Clinicians and Service Users.
AU - Alexis Bennett
AU - Fei Zhao
AU - Henry Yeomans
AU - Andrew Hall
AU - Wen Jiang
PY - 2026
DA - 2026/10/10
JO - Audiology research
DO - 10.3390/audiolres16050157
UR - https://europepmc.org/article/MED/42930355
AB - Background: This pilot survey explored the views of clinicians and service users regarding the use of AI in a specific diagnostic scenario of OME. Methods: An online survey was conducted in collaboration with a Patient and Public Involvement and Engagement panel and advertised through research and clinical networks, social media platforms. Participants (N = 95; 21-72 years) included clinicians (59%) and service users (32%; patients and carers) with experience of OME. Quantitative and qualitative data were collected and analysed, comparing the views of the two groups across the two contexts. Results: Participants demonstrated a generally positive view of AI in OME diagnostics and in healthcare. Clinicians and service users reported comparable levels of trust and acceptance, although clinicians expressed greater concerns about flexibility and accuracy. Perceived key benefits included time and cost savings, while transparency and trust appeared as pivotal facilitators for acceptance. Frequent concerns centred on operational reliability and ethical considerations, with less emphasis on data confidentiality. Conclusions: The findings indicate broad support for AI applications both in OME diagnosis and more widely in healthcare, contingent on addressing concerns about accuracy, human oversight, and ethical use. Recommendations include focused communication strategies emphasising transparency and human oversight in the clinical decision-making processes.
SN - 2039-4330
SN - 2039-4349
ER -