Cross-Check QA: A Quality Assurance Workflow to Prevent Missed Diagnoses by Alerting Inadvertent Discordance Between the Radiologist and Artificial Intelligence in the Interpretation of High-Acuity CT Scans.

Journal: Journal of the American College of Radiology : JACR
PMID: 37423347

Abstract

PURPOSE: The aim of this study was to implement and evaluate a quality assurance (QA) workflow that leverages natural language processing to rapidly resolve inadvertent discordance between radiologists and an artificial intelligence (AI) decision support system (DSS) in the interpretation of high-acuity CT studies when the radiologist does not engage with AI DSS output.

Authors

  • Mariam Chekmeyan
    UMass Chan Medical School, Worcester, Massachusetts. Electronic address: mariam.chekmeyan@umassmed.edu.
  • Steven J Baccei
    Professor, Vice-Chair, Quality, Safety, and Process Improvement, and Interim Co-CMO, UMass Memorial Medical Center and Department of Radiology, UMass Chan Medical School, Worcester, Massachusetts.
  • Elisabeth R Garwood
    Division of Musculoskeletal Imaging and Intervention, Department of Radiology, University of Massachusetts Memorial Medical Center and University of Massachusetts Medical School, Worcester, Massachusetts.

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