Evaluating acute image ordering for real-world patient cases via language model alignment with radiological guidelines.

Journal: Communications medicine
Published Date: Aug 4, 2025

Abstract

BACKGROUND: Diagnostic imaging studies are increasingly important in the management of acutely presenting patients. However, ordering appropriate imaging studies in the emergency department is a challenging task with a high degree of variability among healthcare providers. To address this issue, recent work has investigated whether generative AI and large language models can be leveraged to recommend diagnostic imaging studies in accordance with evidence-based medical guidelines. However, it remains challenging to ensure that these tools can provide recommendations that correctly align with medical guidelines, especially given the limited diagnostic information available in acute care settings.

Authors

  • Michael S Yao
    Department of Bioengineering, University of Pennsylvania, Philadelphia 19104, USA.
  • Allison Chae
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Piya Saraiya
    Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
  • Charles E Kahn
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Walter R Witschey
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • James C Gee
    Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Hersh Sagreiya
    Perelman School of Medicine, University of Pennsylvania, Philadelphia 19104, USA.
  • Osbert Bastani
    University of Pennsylvania, Philadelphia, PA.

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