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Cited by 35Artificial Intelligence in Healthcare and EducationAdvances in Oncology and RadiotherapyTechnology Adoption and User Behaviour

Barriers and facilitators to the adoption of artificial intelligence in radiation oncology: A New Zealand study

Open access
Victor Mugabe, Koki·2021·Technical Innovations & Patient Support in Radiation Oncology
DOI 10.1016/j.tipsro.2021.03.004PMC PMC8085695
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Abstract

Introduction Advances in computing capabilities and automated data collection have led to an increase in the use of Artificial Intelligence (AI) in radiation therapy. This has implications to workflow and workforce planning in radiation oncology departments. A survey was conducted in New Zealand to determine the likelihood of departments adopting AI into their practice. Survey responses were used to determine barriers and facilitators to the adoption of AI. Materials and Methods An online electronic survey was sent to all ten radiation therapy centres in New Zealand. The survey was sent to radiation oncologists, medical physicists and senior radiation therapists involved in treatment planning. Descriptive analysis, factor analysis, analysis of variance and hierarchical multiple regression were used to analyse the data. Results AI usage was low across the country and there was middling expertise. Most respondents found AI had a lot of perceived benefits. On the whole, respondents reported a high likelihood to adopt AI. There were significant differences on the Expertise factor between the staff groups p = 0.016 with radiation therapists reporting more expertise than oncologists. Innovation factors (Perceived Benefit) on their own accounted for over 51 % of total variance and was the biggest predictor of likelihood to adopt AI \( p < 0.001 \) . Organisational factors (Expertise) was a moderate predictor \( p < 0.059 \) . Conclusion The survey results have been used to investigate the barriers and facilitators to the adoption of AI. These results demonstrate that respondents are likely to adopt AI in their practice. Perceived benefits were a facilitator as high scores were correlated with high likelihood of adoption of AI. Low expertise on the other hand was a barrier to adoption as the low scores were linked to lower likelihood of adoption.

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  • Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of AI are key contributors to adoption.

    “Perceived benefit, perceived usefulness, usability, ease of use, usefulness, accuracy, and reliability of the output of the AI are key contributors to adoption [19,45,62-67].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [62]

    The Discussion (page unavailable) identifies perceived benefit as the strongest predictor of AI adoption in this New Zealand radiation-oncology study. The Results regression table does not establish usability or ease of use as predictors, and the passages do not identify output accuracy or reliability as contributors to adoption. Thus the source supports the perceived-benefit part, not the full list. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The presence of AI experts, perhaps a multidisciplinary team with clinical scientists, data science and subject matter experts with AI skills, an innovation manager, AI experts to provide training, and local champions within departments involved in the end-to-end process, was considered an important element for adoption.

    “The presence of AI experts, perhaps a multidisciplinary team, particularly with clinical scientists, data science and subject matter experts with AI skills, an innovation manager, AI experts to provide training, and local champions within departments involved in the end-to-end process, was considered an important element for adoption [32,57,59,62,71].”

    From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [62]

    The Discussion (page not provided) identifies the availability of an AI expert as an adoption factor and recommends training or recruiting staff skilled in AI and data science, alongside collaboration between clinical and data-science expertise. It does not specify an innovation manager, AI experts providing training, or local departmental champions involved throughout the end-to-end process. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 1

  1. [21]E. Rogers, Diffusion of Innovations, New York: Free Press, 2003.

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Authors
Victor Mugabe, K.
Venue
Technical Innovations & Patient Support in Radiation Oncology
Publisher
Elsevier BV
Published
2021-06
Volume
18
Pages
16-21
ISSN
2405-6324 (ISSN-L)
DOI
10.1016/j.tipsro.2021.03.004
PMC
PMC8085695
OpenAlex
W3152780469
Cited by
35
References
21
Citations by year
2026: 8 · 2025: 8 · 2024: 5 · 2023: 10 · 2022: 4
FWCI
1.38
Type
journal article
Language
en
Open access
Licence
Locations
  • Technical Innovations & Patient Support in Radiation Oncology (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advances in Oncology and Radiotherapy
  • Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
Keywords
artificial intelligence adoption, radiation oncology, barriers and facilitators, perceived benefits, radiotherapy treatment planning, radiation therapists, medical physicists, radiation oncologists, organizational factors, innovation drivers

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