Large Language Models in Cancer Imaging: Applications and Future Perspectives.

Journal: Journal of clinical medicine
Published Date: May 8, 2025

Abstract

Recently, there has been tremendous interest on the use of large language models (LLMs) in radiology. LLMs have been employed for various applications in cancer imaging, including improving reporting speed and accuracy via generation of standardized reports, automating the classification and staging of abnormal findings in reports, incorporating appropriate guidelines, and calculating individualized risk scores. Another use of LLMs is their ability to improve patient comprehension of imaging reports with simplification of the medical terms and possible translations to multiple languages. Additional future applications of LLMs include multidisciplinary tumor board standardizations, aiding patient management, and preventing and predicting adverse events (contrast allergies, MRI contraindications) and cancer imaging research. However, limitations such as hallucinations and variable performances could present obstacles to widespread clinical implementation. Herein, we present a review of the current and future applications of LLMs in cancer imaging, as well as pitfalls and limitations.

Authors

  • Mickael Tordjman
    From the Department of Radiology, Boston University School of Medicine, Boston, Mass (A.G., F.W.R., D.H.); Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A.G.); Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland (P.O.); Department of Radiology, Hotel Dieu Hospital and University Paris Cité, Paris, France (M.T.); Department of Radiology, New York University Grossman School of Medicine, New York, NY (J.F., R.K.); Gleamer, Paris, France (N.E.R.); Réseau d'Imagerie Sud Francilien, Clinique du Mousseau Ramsay Santé, Evry, France (N.E.R.); Pôle Médical Sénart, Lieusaint, France (N.E.R.); Department of Radiology and Imaging, Hospital for Special Surgery and Weill Cornell Medicine, New York, NY (J.C.); Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Penn (C.E.K.); Departments of Artificial Intelligence in Biomedical Engineering (F.K.) and Radiology (F.W.R.), Universitätsklinikum Erlangen & Friedrich-Alexander Universität Erlangen-Nürnberg, Erlangen, Germany (F.K.); School of Medicine & Computation, Information and Technology Klinikum rechts der Isar, Technical University Munich, München, Germany (D.R.); Department of Computing, Imperial College London, London, England (D.R.); and Department of Radiology, Tufts Medical Center, Tufts University School of Medicine, Boston, Mass (D.H.).
  • Ian Bolger
    Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
  • Murat Yüce
    Icahn School of Medicine at Mount Sinai Biomedical Engineering and Imaging Institute, New York, USA.
  • Francisco Restrepo
    Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA.
  • Zelong Liu
    Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.
  • Laurent Dercle
    Department of Radiology, Columbia University Medical Center, 630 West 168th Street, New York, NY 10032; Gustave Roussy, Université Paris-Saclay, Université Paris-Saclay, Département D'imagerie Médicale, Villejuif, France.
  • Jeremy McGale
    Radiology, NewYork-Presbyterian/Columbia University Medical Center, New York, New York, USA.
  • Anis L Meribout
    Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
  • Mira M Liu
    Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
  • Arnaud Beddok
    Department of Radiation Oncology, Institut Godinot, 51454 Reims, France.
  • Hao-Chih Lee
    Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mt. Sinai, New York, NY, USA.
  • Scott Rohren
    Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
  • Ryan Yu
    Biomedical Engineering & Imaging Institute, Mount Sinai Health System, New York, NY 10029, USA.
  • Xueyan Mei
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA.
  • Bachir Taouli
    BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA. bachir.taouli@mountsinai.org.

Keywords

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