AI model identifies radiologist-recommended follow-up imaging in reports, has potential for widespread use

The BERT-based model was trained and tested on 6,300 radiology reports and externally validated on an additional 1,260. Its performance in identifying RAI from within the reports was compared to that of a previously developed TLM. 

Out of the entire batch, 10% of the reports contained RAI. In the test set, the BERT-based model achieved precision¬†of 94%, recall of 98% and F1 score of 96%. In comparison, the TML model showed precision of 69%, recall of 65% and F1 score of 67%. The BERT-based model also outperformed TML in accuracy on both the test set and the external validation set,…

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      Radiology AI
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