Retrieval-augmented generation improves precision and trust of a GPT-4 model for emergency radiology diagnosis and classification: a proof-of-concept study - Top-Institutions - MDSpire

Retrieval-augmented generation improves precision and trust of a GPT-4 model for emergency radiology diagnosis and classification: a proof-of-concept study

  • By

  • Anna Fink

  • Johanna Nattenmüller

  • Stephan Rau

  • Alexander Rau

  • Hien Tran

  • Fabian Bamberg

  • Marco Reisert

  • Elmar Kotter

  • Thierno Diallo

  • Maximilian F. Russe

  • February 14, 2025

  • 0 min

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Top Institutions in Radiology

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Trauma Surgery
    • Artificial Intelligence in Medicine

  • #2

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Trauma Radiology
    • Emergency Medicine
    • Medical AI Research

  • #3

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiology
    • Biomedical Informatics
    • AI in Healthcare

  • #4

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Emergency Radiology
    • Trauma Surgery
    • AI and Machine Learning

  • #5

    University of California, San Francisco (UCSF) Medical Center
    University of California, San Francisco (UCSF) Medical Center

    San Francisco, CA

    Key Differentiators

    • Radiology
    • Trauma Care
    • AI in Medical Imaging

Original Source(s)

Retrieval-augmented generation improves precision and trust of a GPT-4 model for emergency radiology diagnosis and classification: a proof-of-concept study

  • by Anna Fink, Johanna Nattenmüller, Stephan Rau, Alexander Rau, Hien Tran, Fabian Bamberg, Marco Reisert, Elmar Kotter, Thierno Diallo, Maximilian F. Russe

  • February 14, 2025

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