The study “Clinical Implementation of a Combined AI and NLP Quality Assurance Program for Pulmonary Nodule Detection in the ED Setting” was recently highlighted in JARC.
The stated objective reads,“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 in order to minimize radiologist interruption.”
The study’s results were as follows:
“Out of 19246 CT exams, 50 exams (0.26%) resulted in secondary review. 34/50 (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 exams of the abdomen and pelvis.”
In conclusion, “a background QA process utilizing 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.”
For more information, visit tinyurl.com/mskd4yfm.
