Machine-learning algorithms could help improve the accuracy of breast cancer screenings when used in combination with assessments from radiologists, according to a new study published in JAMA Network Open.
The study was based on results from the Digital Mammography (DM) DREAM Challenge, a crowd-sourced competition to engage an international scientific community to assess whether artificial intelligence (AI) algorithms could meet or beat radiologist interpretive accuracy.
“Based on our findings, adding AI to radiologists’ interpretation could potentially prevent 500,000 unnecessary diagnostic workups each year in the United States. Robust clinical validation is necessary, however, before any AI algorithm can be adopted broadly,” said Dr. Christoph Lee, professor of radiology at the UW School of Medicine and adjunct professor of health services at the University of Washington School of Public Health. He was the lead radiologist for the Challenge and co-first author of the paper. Dr. Diana Buist of Kaiser Permanente Washington Health Research Institute, an affiliate professor of epidemiology at the UW School of Public Health, is also a co-first author of the paper.Friday Letter Submission, Publish on March 13