Please use this identifier to cite or link to this item: https://repository.monashhealth.org/monashhealthjspui/handle/1/53754
Title: An Australasian survey on the use of ChatGPT and other large language models in medical physics.
Authors: Norris S.;Kron T.;Masterson, Maeve ;Badawy, Mohamed 
Monash Health Department(s): Radiology
Institution: (Norris) Monash Health, Monash Imaging, Melbourne, Australia; Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.
(Kron) Peter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia; Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Australia.
(Masterson) Monash Health, Monash Imaging, Melbourne, Australia.
(Badawy) Monash Health, Monash Imaging, Melbourne, Australia; Department of Medical Imaging and Radiation Sciences, Monash University, Melbourne, Australia
Issue Date: 20-May-2025
Copyright year: 2025
Place of publication: Australia
Publication information: Physical and Engineering Sciences in Medicine. (no pagination). Date of publication: 20 May 2025.
Journal: Physical and Engineering Sciences in Medicine
Abstract: This study surveyed medical physicists in Australia and New Zealand on their use of large language models (LLMs), particularly ChatGPT. There is currently no literature on the application of ChatGPT and other LLMs by medical physicists. This survey targeted a mixed group of professionals, including clinical medical physicists, registrars, students, and other specialised roles. It reveals that many respondents integrate LLM platforms into their work for a broad range of tasks. Most participants reported efficiency gains, although fewer perceived improvements in the overall quality of their work. Despite these benefits, substantial concerns remain regarding data security, patient confidentiality, and the lack of established guidelines or professional training for using these tools in a clinical context. Further, the potential for sudden changes in accessibility and pricing, which could disproportionately impact developing countries and under-resourced departments, implies that other vulnerabilities may exist. These findings suggest the need for the medical physics community to come together and debate the careful balance between exploiting LLM platforms and developing clear best practices that implement robust risk management strategies.
DOI: https://doi.org/10.1007/s13246-025-01571-9
URI: https://repository.monashhealth.org/monashhealthjspui/handle/1/53754
Type: Article
Subjects: radiology
artifical intelligence
large language models
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