Leveraging Large Language Models in Radiology Research: A Comprehensive User Guide.

Journal: Academic radiology
PMID: 39765432

Abstract

Large Language Models (LLMs) such as ChatGPT have been increasingly integrated into radiology research, revolutionizing the research landscape. The Radiology Research Alliance (RRA) of the Association for Academic Radiology (AAR) has convened a Task Force to develop this guide to help radiology researchers responsibly adopt LLM technologies. LLMs can improve various phases of the research process by helping to automate literature reviews, generate research questions, analyze complex datasets, and draft manuscripts. Despite its potential to improve research efficiency, implementation of LLMs poses challenges, especially for users with limited artificial intelligence (AI) experience. This review focuses on approaches to using LLMs in each phase of the research process and addresses prompt engineering to improve interaction with LLMs as well as ethical concerns to ensure scientific integrity. By combining human expertize with AI-driven efficiency, radiology researchers can foster innovation, advance knowledge, and enhance patient care.

Authors

  • Joshua D Brown
    Howard Hughes Medical Institute, University of Maryland Baltimore County, 1000 Hilltop Circle, Baltimore, MD, 21250, USA.
  • Leon Lenchik
    Department of Radiology, Wake Forest School of Medicine, Medical Center Boulevard, Winston-Salem, North Carolina.
  • Fayhaa Doja
    Midwestern University Chicago College of Osteopathic Medicine, Downers Grove, IL 60515 (F.D.). Electronic address: Fayhaa.doja@midwestern.edu.
  • Parisa Kaviani
    Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Court, Suite 248, Boston, Massachusetts.
  • Dallin Judd
    University of North Texas Health Science Center Texas College of Osteopathic Medicine, Fort Worth, TX 76107 (D.J.). Electronic address: Dallinjudd@my.unthsc.edu.
  • Linda Probyn
    Department of Radiology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario.
  • Sterling Lee
    University of Colorado School of Medicine, Aurora, CO 80045 (S.L.). Electronic address: Sterling.lee@cuanschutz.edu.
  • Eric M Goodman
    Department of Radiology, Northwell, 2000 Marcus Ave, Suite 300, New Hyde Park, NY 11042-1069 (E.M.G.). Electronic address: Egoodman2@northwell.edu.
  • Ashkan Eighaei Sedeh
    Department of Radiology, Capital Health Medical Center, 1 Capital way, Pennington, NJ 08534 (A.E.S.). Electronic address: Aeighaeisedeh@capitalhealth.org.
  • Mina S Makary
    Division of Vascular and Interventional Radiology, Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, OH, United States of America.
  • Ryan K Lee
  • Michele Retrouvey
    Department of Radiology, Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL 33431 (M.R.). Electronic address: Mretrouvey@health.fau.edu.

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