The accuracy of digital mammograms is no different, regardless of whether a human or a commercially available AI-based system reads them.
That’s the claim made in a new study that found that AI-based readings are equivalent to those of human radiologists, suggesting that greater implementation of such systems is necessary and could prove helpful in reducing large numbers of patients waiting to be screened.
"Before we could decide what is the best way for AI systems to be introduced in the realm of breast cancer screening with mammography, we wanted to know how good can these systems really be," Ioannis Sechopoulos, one of the paper's authors, said in a statement. "It was exciting to see that these systems have reached the level of matching the performance of not just radiologists, but of radiologists who spend at least a substantial portion of their time reading screening mammograms."
Breast cancer is responsible for approximately 500,000 deaths worldwide annually, an issue that has prompted clinicians to investigate other potential screening methods. While in development since the 1990s for breast lesion detection in mammography, no studies have shown improvements in performance or cost effectiveness with AI-based systems, hindering their adoption as a method, according to the study authors.
The hope of many is that use of these systems could potentially reduce the high labor intensity that current screening programs face due to the large number of women who require examinations.
The study examined the use of AI at a case level, comparing the performance of a commercially available system to that of 101 radiologists who scored nine distinct cohorts of mammograms from four different manufacturers as part of studies performed in the past for other reasons. A total of 2,652 exams were assessed and broken down into data sets, each of which consisted of findings that came from systems of four different vendors, and were assessed by multiple radiologists per exam. The combined number of interpretations by the 101 radiologists was 28,296.
The findings of the system were found to be statistically non-inferior to that of the average radiologist out of the 101, achieving a cancer detection accuracy on par with an average breast radiologist in the retrospective setting.
