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Updated: Aug 11, 2025
Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules
Published on: May 19, 2023
Clinical Implementation of a Combined Artificial Intelligence and Natural Language Processing Quality Assurance
Joseph J Cavallo1, Irene de Oliveira Santo2, Jonathan L Mezrich1
1Assistant Director of Informatics and Assistant Medical Director of Clinical Affairs, Yale Radiology, Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut; Yale Department of Radiology and Biomedical Imaging, Yale New Haven Hospital, New Haven, Connecticut.
Objective:
This quality assurance study assessed the implementation of a combined artificial intelligence (AI) and natural language processing (NLP) program for pulmonary nodule detection in the emergency department setting. The program was designed to function outside of normal reading workflows to minimize radiologist interruption.
Materials And Methods:
In all, 19,246 CT examinations including at least some portion of the lung anatomy performed in the emergent setting from October 1, 2021, to June 1, 2022, were processed by the combined AI-NLP program. The program used an AI algorithm trained on 6-mm to 30-mm pulmonary nodules to analyze CT images and an NLP to analyze radiological reports. Cases flagged as negative for pulmonary nodules by the NLP but positive by the AI algorithm were classified as suspected discrepancies. Discrepancies result in secondary review of examinations for possible addenda.
Results:
Out of 19,246 CT examinations, 50 examinations (0.26%) resulted in secondary review, and 34 of 50 (68%) reviews resulted in addenda. Of the 34 addenda, 20 patients received instruction for new follow-up imaging. Median time to addendum was 11 hours. The majority of reviews and addenda resulted from missed pulmonary nodules on CT examinations of the abdomen and pelvis.
Conclusion:
A background quality assurance process using AI and NLP helped improve the detection of pulmonary nodules and resulted in increased numbers of patients receiving appropriate follow-up imaging recommendations. This was achieved without disrupting in-shift radiologist workflow or causing significant delays in patient follow for the diagnosed pulmonary nodule.
