Empowering PET imaging reporting with retrieval-augmented large language models and reading reports database: a pilot single center study.

Journal: European journal of nuclear medicine and molecular imaging
Published Date:

Abstract

PURPOSE: The potential of Large Language Models (LLMs) in enhancing a variety of natural language tasks in clinical fields includes medical imaging reporting. This pilot study examines the efficacy of a retrieval-augmented generation (RAG) LLM system considering zero-shot learning capability of LLMs, integrated with a comprehensive database of PET reading reports, in improving reference to prior reports and decision making.

Authors

  • Hongyoon Choi
    Cheonan Public Health Center, 234-1 Buldang-Dong, Seobuk-Gu, Cheonan, Republic of Korea.
  • Dongjoo Lee
    Portrai, Inc., Seoul, Republic of Korea.
  • Yeon-Koo Kang
    Department of Nuclear Medicine, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.
  • Minseok Suh
    Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.