Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM) - Top-Institutions - MDSpire

Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)

  • By

  • Zhongnan Fang

  • Andrew Johnston

  • Lina Y. Cheuy

  • Hye Sun Na

  • Magdalini Paschali

  • Camila Gonzalez

  • Bonnie A. Armstrong

  • Arogya Koirala

  • Derrick Laurel

  • Andrew Walker Campion

  • Michael Iv

  • Akshay S. Chaudhari

  • David B. Larson

  • October 16, 2025

  • 0 min

Share

Top Institutions in General Medicine

Brief introduction explaining scope and methodology.

  • #1

    Massachusetts General Hospital
    Massachusetts General Hospital

    Boston, MA

    Key Differentiators

    • Radiology
    • Neuroradiology
    • Artificial Intelligence

  • #2

    Stanford University Medical Center
    Stanford University Medical Center

    Stanford, CA

    Key Differentiators

    • Radiology
    • Neuroradiology
    • Machine Learning

  • #3

    Johns Hopkins Hospital
    Johns Hopkins Hospital

    Baltimore, MD

    Key Differentiators

    • Radiology
    • Neuroradiology
    • Biomedical Engineering

  • #4

    Mayo Clinic
    Mayo Clinic

    Rochester, MN

    Key Differentiators

    • Radiology
    • Neuroradiology
    • Clinical AI Implementation

  • #5

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

    San Francisco, CA

    Key Differentiators

    • Radiology
    • Neuroradiology
    • AI in Healthcare

Original Source(s)

Automated real-time assessment of intracranial hemorrhage detection AI using an ensembled monitoring model (EMM)

  • by Zhongnan Fang, Andrew Johnston, Lina Y. Cheuy, Hye Sun Na, Magdalini Paschali, Camila Gonzalez, Bonnie A. Armstrong, Arogya Koirala, Derrick Laurel, Andrew Walker Campion, Michael Iv, Akshay S. Chaudhari, David B. Larson

  • October 16, 2025

Related Content

Interpretable deep learning for multicenter gastric cancer T staging from CT images

Anatomically-guided Masked Autoencoder with Domain-Adaptive Prompting (AMAP) for multimodal cerebral aneurysm detection and segmentation

Deep multimodal state-space fusion of endoscopic-radiomic and clinical data for survival prediction in colorectal cancer