A commercial artificial intelligence (AI) tool used off-label was effective at excluding pathology and had equal or lower rates of critical misses on chest X-ray than radiologists, according to a study published today in Radiology , a journal of the Radiological Society of North America (RSNA). Recent developments in AI have sparked a growing interest in computer-assisted diagnosis, partly motivated by the increasing workload faced by radiology departments, the global shortage of radiologists and the potential for burnout in the field. Radiology practices have a high volume of unremarkable (no clinically significant findings) chest X-rays, and AI could possibly improve workflow by providing an automatic report.
Researchers in Denmark set out to estimate the proportion of unremarkable chest X-rays where AI could correctly exclude pathology without increasing diagnostic errors. The study included radiology reports and data from 1,961 patients (median age, 72 years; 993 female), with one chest X-ray per patient, obtained from four Danish hospitals. Our group and others have previously shown that AI tools are capable of excluding pathology in chest X-rays with high confidence and thereby provide an autonomous normal report without a human in-the-loop.
Such AI algorithms miss very few abnormal chest radiographs. However, before our current study, we didn't know what the appropriate threshold was for these models." Louis Lind Plesner, M.
D., lead author from the Department of Radiol.