Comparison of CT referral justification using clinical decision support and large language models in a large European cohort.

Journal: European radiology
Published Date: Apr 27, 2025

Abstract

BACKGROUND: Ensuring appropriate use of CT scans is critical for patient safety and resource optimization. Decision support tools and artificial intelligence (AI), such as large language models (LLMs), have the potential to improve CT referral justification, yet require rigorous evaluation against established standards and expert assessments.

Authors

  • Mor Saban
    School of Health Sciences, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel. Morsaban1@tauex.tau.ac.il.
  • Yaniv Alon
    School of Health Sciences, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
  • Osnat Luxenburg
    Medical Technology, Health Information and Research Directorate, Ministry of Health, Jerusalem, Israel.
  • Clara Singer
    School of Health Sciences, Faculty of Medical and Health Sciences, Tel Aviv University, Tel Aviv, Israel.
  • Monika Hierath
    European Society of Radiology, Vienna, Austria.
  • Alexandra Karoussou Schreiner
    Radiation Protection Department, Health Directorate, Ministry of Health, Luxembourg City, Luxembourg.
  • Boris Brkljačić
    Department of Radiology, University Hospital Dubrava, University of Zagreb School of Medicine, Zagreb, Croatia.
  • Jacob Sosna
    Department of Radiology, Hadassah Medical Center, Faculty of Medicine, Hebrew University of Jerusalem, Jerusalem, Israel.

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