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Cross-Check QA: a quality assurance workflow to prevent missed diagnoses by alerting inadvertent discordance between the radiologist and AI in the interpretation of high acuity CT scans.

Chekmeyan M, Baccei SJ, Garwood ER. Cross-Check QA: a quality assurance workflow to prevent missed diagnoses by alerting inadvertent discordance between the radiologist and AI in the interpretation of high acuity CT scans. J Am Coll Radiol. 2023;20(12):1225-1230. doi:10.1016/j.jacr.2023.06.010.

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October 4, 2023
Chekmeyan M, Baccei SJ, Garwood ER. J Am Coll Radiol. 2023;20(12):1225-1230.
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Artificial intelligence (AI) has become a useful tool to support radiologists in diagnostic imaging. In this study, discordant findings between the radiologist and AI (negative by radiologist report, positive by AI report, with unviewed AI decision support system output) triggered an automatic manual review of the diagnostic images. More than 111,000 CT studies were analyzed, with 46 triggering the automatic review; of those, 26 (0.02%) were true positives (i.e., missed diagnosis by radiologist but identified by AI).

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Chekmeyan M, Baccei SJ, Garwood ER. Cross-Check QA: a quality assurance workflow to prevent missed diagnoses by alerting inadvertent discordance between the radiologist and AI in the interpretation of high acuity CT scans. J Am Coll Radiol. 2023;20(12):1225-1230. doi:10.1016/j.jacr.2023.06.010.

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