A retrospective study compared discrepancies between CT reports and AI-based detection of ICH. All non-contrast CT brain cases were collected over two months; cases reported as negative for ICH were included (n=1,812), while cases reported as suspicious for ICH (n=504) were excluded. Fellowship-trained NRs finalized all reports. An AI-tool trained to detect ICH was then run on the case set.
Upon final consensus, 22 cases of ICH detected by AI were missed on finalized reports, yielding an error rate of 4.2% (22/ (504+22)).
AI-augmentation can flag potential discrepancies between scans and their reports, possibly improving the quantification of error rates among practicing radiologists.
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