Large language models (LLM) and ChatGPT: what will the impact on nuclear medicine be?
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The need to rethink and revise current assessment tools in health care education comes in light of ChatGPT's ability to pass reputable exams (e.g., USMLE) and the possibility of ChatGPT misuse resulting in academic dishonesty.
“The need to rethink and revise the current assessment tools in health care education comes in light of ChatGPT’s ability to pass reputable exams (e.g., USMLE) and possibility of ChatGPT misuse, which would result in academic dishonesty [24,34,58,59,62,76,85,86,87].”
From ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns (2023) · cites this paper as [86]
The paper reports that ChatGPT passed or came close to passing all three USMLE parts and discusses the possibility of dishonest use in an online nuclear medicine exam. Its conclusion says clinician education and training will need to adapt, but it does not specifically call for revising current assessment tools; it also finds no present threat to the integrity of online nuclear medicine examinations. (LLM, conf=0.86)
Experimental — accuracy not yet measuredAlberts et al. (2023) emphasized that adversarial testing in medical QA must account for the inherent complexity of the domain, noting that even slight modifications in phrasing can significantly alter clinical interpretations.
“Alberts et al. (2023) emphasized that adversarial testing in medical QA must account for the inherent complexity of the domain, noting that even slight modifications in phrasing can significantly alter clinical interpretations.”
From Trustworthy Medical Question Answering: An Evaluation-Centric Survey (2025) · cites this paper as Alberts et al., 2023
In the paper’s discussion of its nuclear-medicine question test (page unavailable), Alberts et al. describe ChatGPT as context-sensitive and report different, incorrect answers after a question was rephrased. They also discuss adversarial examples and the difficulty of constructing them for LLMs. They do not say that adversarial testing must account for the inherent complexity of medical QA, or establish that slight phrasing changes significantly alter clinical interpretations; their example involved both rephrasing and corrective feedback. (LLM, conf=0.84)
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- Authors
- Alberts, I. L., Mercolli, L., Pyka, T., Prenosil, G., Shi, K., Rominger, A., & Afshar-Oromieh, A.
- Affiliations
- University of Bern (CH)
- Corresponding author
- Alberts, Ian L.
- Venue
- European Journal of Nuclear Medicine and Molecular Imaging
- Published
- 2023-03-09
- Volume
- 50
- Issue
- 6
- Pages
- 1549-1552
- ISSN
- 1619-7070 (ISSN-L)
- 1619-7089
- PMC
- PMC9995718
- OpenAlex
- W4323652488
- Cited by
- 207
- References
- 31
- Citations by year
- 2026: 32 · 2025: 66 · 2024: 72 · 2023: 36
- FWCI
- 8.23
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- European Journal of Nuclear Medicine and Molecular Imaging (publishedVersion, other-oa)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Bern Open Repository and Information System (University of Bern) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Keywords
- large language models, ChatGPT, artificial intelligence, nuclear medicine
- MeSH terms
- Humans, Nuclear Medicine, Radionuclide Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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