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Review ArticleArtificial Intelligence
Open Access

A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead

Neetu Soni, Manish Ora, Amit Agarwal, Tianbao Yang and Girish Bathla
American Journal of Neuroradiology July 2025, 46 (7) 1292-1299; DOI: https://doi.org/10.3174/ajnr.A8589
Neetu Soni
aFrom the Department of Radiology (N.S., A.A.), Mayo Clinic—Jacksonville, Jacksonville, Florida
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Manish Ora
bDepartment of Nuclear Medicine (M.O.), Sanjay Gandhi Post Graduate Institute of Medical Science, Lucknow, India
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Amit Agarwal
aFrom the Department of Radiology (N.S., A.A.), Mayo Clinic—Jacksonville, Jacksonville, Florida
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Tianbao Yang
cDepartment of Computer Science & Engineering (T.Y.), Texas A&M University, College Station, Texas
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Girish Bathla
dDepartment of Radiology (G.B.), Mayo Clinic, Rochester, Minnesota
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    FIG 1.

    Bar chart showing the training tokens and parameters (in billions) of some of the common LLMs. Please note that several models are essentially part of larger families of models, and individual models may have variability in training tokens and parameters (data source: https://lifearchitect.ai/).

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American Journal of Neuroradiology: 46 (7)
American Journal of Neuroradiology
Vol. 46, Issue 7
1 Jul 2025
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Cite this article
Neetu Soni, Manish Ora, Amit Agarwal, Tianbao Yang, Girish Bathla
A Review of the Opportunities and Challenges with Large Language Models in Radiology: The Road Ahead
American Journal of Neuroradiology Jul 2025, 46 (7) 1292-1299; DOI: 10.3174/ajnr.A8589

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Large Language Models in Radiology
Neetu Soni, Manish Ora, Amit Agarwal, Tianbao Yang, Girish Bathla
American Journal of Neuroradiology Jul 2025, 46 (7) 1292-1299; DOI: 10.3174/ajnr.A8589
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  • Article
    • Abstract
    • ABBREVIATIONS:
    • Models, Modals, and Miscellaneous Things in between!
    • Potential Applications of LLMs in Radiology
    • LLM Limitations and Drawbacks
    • Future Directions
    • CONCLUSIONS
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