Evidence map›Paper›PMID 40392469›Full record

ArticlePhysical and engineering sciences in medicine2025

An Australasian survey on the use of ChatGPT and other large language models in medical physics.

Stanley A Norris, Tomas Kron, Maeve Masterson, Mohamed K Badawy

Abstract read
In one paragraph

Article in Physical and engineering sciences in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Stanley A NorrisMonash Health, Monash Imaging, Melbourne, Australia. stanley.norris@monashhealth.org.ORCID http://orcid.org/0009-0001-2801-5074
Tomas KronPeter MacCallum Cancer Centre, Department of Physical Sciences, Melbourne, Australia.ORCID http://orcid.org/0000-0002-5189-4046
Maeve MastersonMonash Health, Monash Imaging, Melbourne, Australia.
Mohamed K BadawyMonash Health, Monash Imaging, Melbourne, Australia.ORCID http://orcid.org/0000-0001-8029-9951

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Health PhysicsLanguageAustraliaGenerative Artificial IntelligenceHumansLarge Language ModelsNew ZealandSurveys and QuestionnairesChatGPTGenerative artificial intelligenceLarge language modelMedical physics

Identifiers

PMID40392469
PMCPMC12511241

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.