AI-Assisted Summarization of Radiologic Reports: Evaluating GPT3davinci, BARTcnn, LongT5booksum, LEDbooksum, LEDlegal, and LEDclinical.

Journal: AJNR. American journal of neuroradiology
PMID: 38238092

Abstract

BACKGROUND AND PURPOSE: The review of clinical reports is an essential part of monitoring disease progression. Synthesizing multiple imaging reports is also important for clinical decisions. It is critical to aggregate information quickly and accurately. Machine learning natural language processing (NLP) models hold promise to address an unmet need for report summarization.

Authors

  • Aichi Chien
    From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California aichi@ucla.edu.
  • Hubert Tang
    From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California.
  • Bhavita Jagessar
    From the Department of Radiological Science (A.C., H.T., B.J., K.N., N.S.), David Geffen School of Medicine at UCLA, Los Angeles, California.
  • Kai-Wei Chang
    1 Department of Computer Science, University of California, Los Angeles, California.
  • Nanyun Peng
    Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Marina del Rey, CA, USA.
  • Kambiz Nael
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.
  • Noriko Salamon
    Department of Radiology, University of California, Los Angeles, Los Angeles, CA, USA.

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