ChatGPT in Medical Imaging Higher Education
Abstract
Introduction Academic integrity among radiographers and nuclear medicine technologists/scientists in both higher education and scientific writing has been challenged by advances in artificial intelligence (AI). The recent release of ChatGPT, a chatbot powered by GPT-3.5 capable of producing accurate and human-like responses to questions in real-time, has redefined the boundaries of academic and scientific writing. These boundaries require objective evaluation.Method ChatGPT was tested against six subjects across the first three years of the medical radiation science undergraduate course for both exams (n = 6) and written assignment tasks (n = 3). ChatGPT submissions were marked against standardised rubrics and results compared to student cohorts. Submissions were also evaluated by Turnitin for similarity and AI scores.Results ChatGPT powered by GPT-3.5 performed below the average student performance in all written tasks with an increasing disparity as subjects advanced. ChatGPT performed better than the average student in foundation or general subject examinations where shallow responses meet learning outcomes. For discipline specific subjects, ChatGPT lacked the depth, breadth, and currency of insight to provide pass level answers.Conclusion ChatGPT simultaneously poses a risk to academic integrity in writing and assessment while affording a tool for enhanced learning environments. These risks and benefits are likely to be restricted to learning outcomes of lower taxonomies. Both risks and benefits are likely to be constrained by higher order taxonomies.Implications for practice ChatGPT powered by GPT3.5 has limited capacity to support student cheating, introduces errors and fabricated information, and is readily identified by software as AI generated. Lack of depth of insight and appropriateness for professional communication also limits capacity as a learning enhancement tool.
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ChatGPT performs well in exams in disciplines such as engineering, economics, medicine, and geography.
“Related research shows that ChatGPT performs well in exams in disciplines such as engineering (Uhlig et al., 2023), economics (Geerling et al., 2023), medicine (Currie et al., 2023), and geography (Stutz et al., 2023), indicating that the issue of academic integrity in higher education is facing unprecedented challenges.”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Currie et al., 2023
ChatGPT has the potential to encourage cheating and academic dishonesty.
“The major concern is that ChatGPT has the potential to encourage cheating and academic dishonesty (Crawford et al., 2023; Currie et al., 2023; Gorichanaz, 2023; Naidu & Sevnarayan, 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Currie et al., 2023
ChatGPT offers opportunities for authentic assessment by challenging students' beliefs and critical thinking.
“Moreover, ChatGPT offers opportunities for authentic assessment by challenging students’ beliefs and critical thinking (Crawford et al., 2023; Currie et al., 2023; Geerling et al., 2023).”
From A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education (2024) · cites this paper as Currie et al., 2023
ChatGPT can provide initial observations that highlight regions of interest and assist radiologists in their analyses.
“It has the ability to provide initial observations, which can highlight regions of interest and assist radiologists in their analyses [13,25,26,27,28,29,30,31,32,33].”
ChatGPT has the ability to analyse and generate descriptions for medical images, which can enhance the workflow of radiologists.
“The potential of ChatGPT lies in its ability to analyse and generate descriptions for medical images, which can enhance the workflow of radiologists.”
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ChatGPT in medical imaging higher education: Reply to Currie et al.
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Pedagogical Transformation Using Large Language Models in a Cybersecurity Course
Ostos, R., Félix, V. G., Mena, L. J., Toral-Cruz, H., Ochoa-Brust, A. M., González, A., Félix, R. A., Pacheco, J. C. R., Flores, V., & Martínez-Peláez, R. (2026) · AI · doi:10.3390/ai7010025 · Cited by 1 · see citing works
- Authors
- Currie, G., Singh, C., Nelson, T., Nabasenja, C., Al-Hayek, Y., & Spuur, K.
- Affiliations
- Charles Sturt University (AU)
- Corresponding author
- Currie, G.
- Venue
- Radiography
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 29
- Issue
- 4
- Pages
- 792-799
- ISSN
- 1078-8174 (ISSN-L)
- OpenAlex
- W4379136378
- Cited by
- 128
- References
- 6
- Citations by year
- 2026: 19 · 2025: 34 · 2024: 49 · 2023: 26
- FWCI
- 31.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Radiography (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Social Sciences › Safety Research › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Keywords
- ChatGPT, academic integrity, artificial intelligence in education, radiographer training, nuclear medicine technologists, academic dishonesty, written assessment, AI-generated text detection, Turnitin, GPT-3.5, higher education
- MeSH terms
- Artificial Intelligence, Diagnostic Imaging, Humans, Learning, Radiography, Software
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