IPEM topical report: results of a 2024 UK survey of artificial intelligence in medical physics and clinical engineering
IPEM topical report: results of a 2024 UK survey of artificial intelligence in medical physics and clinical engineering
Medical physics and clinical engineering (MPCE) professionals have a critical role in the safe and effective deployment of artificial intelligence (AI) in healthcare, however their attitudes and opinions towards AI are not well understood. A 2024 survey was launched by the Institute of Physics and Engineering in Medicine to UK MPCE professionals to gather information on the current usage of AI, whether it is believed their role will change, if there is any fear about job replacement, the training being conducted, levels of preparedness, concerns about AI introduction, and barriers to AI deployment. A total of 409 responses were received. It was found that AI is widely used (59% of respondents), with wide disparities between disciplines (radiotherapy 76% compared to clinical engineering 37%). Job losses are predicted by 40% of staff, with junior NHS staff more concerned. Nearly 80% of respondents are investing in their own learning, but only 23% know where to look for training resources. Only 10% of the cohort had some prior AI education. Without prior education on AI, only 13% of respondents feel prepared for AI introduction; but this increases by a factor of three with education. Lack of training and knowledge is the major concern and barrier to AI adoption, while lack of a clear AI governance framework was also frequently cited. This survey provides a snapshot of the current status and attitudes of the UK MPCE workforce towards AI and should be used in guiding future efforts in training and education, addressing discipline disparities and overcoming deployment barriers.
AI, UK, survey, workforce
Doolan, Paul
43cff410-b3c2-4abe-8d45-094adb4559b5
Michopoulou, Sofia
f21ba2a3-f5d3-4998-801f-1ae72ff5d92c
Meades, Richard
5222c9d9-524a-4a6e-9ca7-2c3d4f4b0d9a
20 July 2025
Doolan, Paul
43cff410-b3c2-4abe-8d45-094adb4559b5
Michopoulou, Sofia
f21ba2a3-f5d3-4998-801f-1ae72ff5d92c
Meades, Richard
5222c9d9-524a-4a6e-9ca7-2c3d4f4b0d9a
Doolan, Paul, Michopoulou, Sofia and Meades, Richard
(2025)
IPEM topical report: results of a 2024 UK survey of artificial intelligence in medical physics and clinical engineering.
Physics in Medicine & Biology, 70 (14), [14TR01].
(doi:10.1088/1361-6560/ade92b).
Abstract
Medical physics and clinical engineering (MPCE) professionals have a critical role in the safe and effective deployment of artificial intelligence (AI) in healthcare, however their attitudes and opinions towards AI are not well understood. A 2024 survey was launched by the Institute of Physics and Engineering in Medicine to UK MPCE professionals to gather information on the current usage of AI, whether it is believed their role will change, if there is any fear about job replacement, the training being conducted, levels of preparedness, concerns about AI introduction, and barriers to AI deployment. A total of 409 responses were received. It was found that AI is widely used (59% of respondents), with wide disparities between disciplines (radiotherapy 76% compared to clinical engineering 37%). Job losses are predicted by 40% of staff, with junior NHS staff more concerned. Nearly 80% of respondents are investing in their own learning, but only 23% know where to look for training resources. Only 10% of the cohort had some prior AI education. Without prior education on AI, only 13% of respondents feel prepared for AI introduction; but this increases by a factor of three with education. Lack of training and knowledge is the major concern and barrier to AI adoption, while lack of a clear AI governance framework was also frequently cited. This survey provides a snapshot of the current status and attitudes of the UK MPCE workforce towards AI and should be used in guiding future efforts in training and education, addressing discipline disparities and overcoming deployment barriers.
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Published date: 20 July 2025
Keywords:
AI, UK, survey, workforce
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Local EPrints ID: 505084
URI: http://eprints.soton.ac.uk/id/eprint/505084
PURE UUID: 3fb165d7-4abf-4454-a439-2f71b28ab3d7
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Date deposited: 25 Sep 2025 17:10
Last modified: 25 Sep 2025 17:10
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Author:
Paul Doolan
Author:
Sofia Michopoulou
Author:
Richard Meades
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