AI Adoption, Adaptation, and Workforce Sustainability in Medical Imaging: Systematic Review and Conceptual Framework
6 affiliations
- 1Centre for Work, Organisation and Wellbeing, Griffith University, 170 Kessels Road, Nathan, Brisbane, Queensland, 4111, Australia, 61 0737354856
- 2Griffith University, Gold Coast, Queensland, Australia
- 3School of Medicine and Dentistry, Griffith University, Gold Coast, Australia
- 4Gold Coast Health, Gold Coast, Australia
- 5QUT Business School, Queensland University of Technology, Brisbane, Australia
- 6Gold Coast University Hospital, Gold Coast Health, Gold Coast, Australia
Abstract
Background: Despite growing recognition that AI-driven systems offer transformative benefits for diagnostic imaging, insufficient evidence exists regarding how medical imaging workforces adapt to AI technology to support workforce sustainability.
Objective: The aim of this study is to systematically review the existing literature on perceptions and attitudes toward AI adoption in medical imaging, postadoption adaptation behaviors within the workforce, and the implications of these processes for workforce sustainability.
Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search was carried out across 7 databases. Thematic analysis was used to systematically identify, analyze, and interpret patterns across the literature, providing a comprehensive understanding of workforce perceptions and adaptation behaviors.
Results: A total of 69 peer-reviewed articles were included for thematic synthesis. The findings produced 6 critical factors that were identified as themes associated with AI adoption: perceived benefits, trust, readiness to change, autonomy and control, perceived threat to professional identity, and attitudes and intentions toward AI adoption and use. Four key facets of effective cognitive and behavioral adaptation were identified: training and skills development, task performance during AI-assisted practice, reduction of mental health burden and work strain, and policy compliance. The influence of work engagement and job satisfaction on workforce sustainability was also conceptualized. The findings were integrated into a framework describing the radiology workforce perspectives on AI adoption, adaptation behaviors, and workforce sustainability.
Conclusions: The findings have potential implications for policy, particularly in strengthening AI-equipped medical imaging systems, fostering trust in AI-driven technologies, and supporting workforce training and upskilling, while considering professional autonomy and role identity. The review highlights a research gap, emphasizing the need for further investigation into postadoption adaptation processes and informing policy development that links AI integration with long-term workforce sustainability in medical imaging.
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BibTeX
@article{brough2026adoption,
title = {{AI Adoption, Adaptation, and Workforce Sustainability in Medical Imaging: Systematic Review and Conceptual Framework}},
author = {Paula Brough and Khondker Mohammad Zobair and Javad Pool and Natalie Haskell and Luke Houghton and Paul Scuffham and Jennifer Loy and Ashlea Troth and Salim Memon and Rachael Smithson and Maria Khan and Brooke Murphy and Letisha Living and Leanne Du and John Richards and Jennifer Dolkens and Matthew Aitken and Jason Zhang and John Grieve and Sandeep Bhuta},
journal = {JMIR AI},
year = {2026},
month = nov,
doi = {10.2196/101944},
url = {https://doi.org/10.2196/101944},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AI Adoption, Adaptation, and Workforce Sustainability in Medical Imaging: Systematic Review and Conceptual Framework
AU - Paula Brough
AU - Khondker Mohammad Zobair
AU - Javad Pool
AU - Natalie Haskell
AU - Luke Houghton
AU - Paul Scuffham
AU - Jennifer Loy
AU - Ashlea Troth
AU - Salim Memon
AU - Rachael Smithson
AU - Maria Khan
AU - Brooke Murphy
AU - Letisha Living
AU - Leanne Du
AU - John Richards
AU - Jennifer Dolkens
AU - Matthew Aitken
AU - Jason Zhang
AU - John Grieve
AU - Sandeep Bhuta
PY - 2026
DA - 2026/11/03
JO - JMIR AI
DO - 10.2196/101944
UR - https://doi.org/10.2196/101944
AB - Background: Despite growing recognition that AI-driven systems offer transformative benefits for diagnostic imaging, insufficient evidence exists regarding how medical imaging workforces adapt to AI technology to support workforce sustainability. Objective: The aim of this study is to systematically review the existing literature on perceptions and attitudes toward AI adoption in medical imaging, postadoption adaptation behaviors within the workforce, and the implications of these processes for workforce sustainability. Methods: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a systematic search was carried out across 7 databases. Thematic analysis was used to systematically identify, analyze, and interpret patterns across the literature, providing a comprehensive understanding of workforce perceptions and adaptation behaviors. Results: A total of 69 peer-reviewed articles were included for thematic synthesis. The findings produced 6 critical factors that were identified as themes associated with AI adoption: perceived benefits, trust, readiness to change, autonomy and control, perceived threat to professional identity, and attitudes and intentions toward AI adoption and use. Four key facets of effective cognitive and behavioral adaptation were identified: training and skills development, task performance during AI-assisted practice, reduction of mental health burden and work strain, and policy compliance. The influence of work engagement and job satisfaction on workforce sustainability was also conceptualized. The findings were integrated into a framework describing the radiology workforce perspectives on AI adoption, adaptation behaviors, and workforce sustainability. Conclusions: The findings have potential implications for policy, particularly in strengthening AI-equipped medical imaging systems, fostering trust in AI-driven technologies, and supporting workforce training and upskilling, while considering professional autonomy and role identity. The review highlights a research gap, emphasizing the need for further investigation into postadoption adaptation processes and informing policy development that links AI integration with long-term workforce sustainability in medical imaging.
SN - 2817-1705
ER -