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Cited by 135Academic integrity and plagiarismArtificial Intelligence in Healthcare and EducationArtificial Intelligence in Education

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

ChatGPT in medical imaging higher education

Currie, G., Singh, C., Nelson, T., Nabasenja, C., Al-Hayek, Y., Spuur, K.·2023·Radiography
DOI 10.1016/j.radi.2023.05.011
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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.

Overview

Integrity signals

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Crossref · 10.1016/j.radi.2023.05.011

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Metadata

Authors
Currie, G., Singh, C., Nelson, T., Nabasenja, C., Al-Hayek, Y., & Spuur, K.
Affiliations
Charles Sturt University (AU)
Corresponding author
Currie, G.
ORCID
Venue
Radiography
Publisher
Elsevier BV
Published
2023-07
Volume
29
Issue
4
Pages
792-799
ISSN
1078-8174 (ISSN-L)
DOI
10.1016/j.radi.2023.05.011
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
Licence
Locations
  • Radiography (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Social Sciences › Education › Academic integrity and plagiarism
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Education › 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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