Evaluation of large language models performance against humans for summarizing MRI knee radiology reports: A feasibility study.

Journal: International journal of medical informatics
Published Date:

Abstract

OBJECTIVES: This study addresses the critical need for accurate summarization in radiology by comparing various Large Language Model (LLM)-based approaches for automatic summary generation. With the increasing volume of patient information, accurately and concisely conveying radiological findings becomes crucial for effective clinical decision-making. Minor inaccuracies in summaries can lead to significant consequences, highlighting the need for reliable automated summarization tools.

Authors

  • Pilar López-Úbeda
    Universidad de Jaén, Jaén, Andalucía, Spain.
  • Teodoro Martín-Noguerol
    MRI Unit, Radiology Department, HT médica Carmelo Torres 2, Jaén 23007, Spain. Electronic address: t.martin.f@htime.org.
  • Carolina Díaz-Angulo
    MRI Unit, Radiology Department, Health Time, Gijón, Spain. Electronic address: c.diaz@htmedica.com.
  • Antonio Luna
    MRI Unit, Radiology Department, Health Time, Jaén, Spain. Electronic address: aluna70@htime.org.