Mammography screening exams are some of the most challenging medical images to interpret, but due to staffing issues many mammograms are read by general radiologists rather than specialists. A new study in Radiology found that an AI-based workflow helped close the gap, leading to a 25% improvement in the cancer detection rate for general radiologists.
Most of the big population-based studies on mammography AI have been conducted in Europe, where breast screening is performed under a double-reader paradigm that has two radiologists interpreting exams.
- In this scenario, studies have shown that AI can eliminate the need for a second reader, cutting workforce requirements with the same or even better cancer detection rates.
But U.S. breast screening programs don’t use double-reading, leaving many to wonder where AI fits into the single-reader paradigm – especially when that reader is a general radiologist with no breast fellowship training.
- The new study offers some clarity. Researchers developed an AI-based workflow that integrated DeepHealth’s ProFound Pro 2.x deep-learning application into DBT-based screening programs.
They set up AI as a “safeguard review.” After a radiologist’s initial interpretation, AI analyzed non-recalled mammograms for suspicion of cancer. Exams that exceeded ProFound’s risk threshold were flagged and routed to an expert reviewer.
- If the reviewer agreed with AI, the original interpreting radiologist was consulted and had final authority on whether to recall the case.
The safeguard review concept was tested at 109 breast imaging facilities that saw 578k DBT mammography exams from 2021 to 2022. In particular, researchers focused on the impact the safeguard review had on interpretation accuracy of both specialists and general radiologists, finding…
- The cancer detection rate of generalists improved 25%, from 3.76 to 4.99 cancers per 1k exams.
- The CDR of specialists did not change at a statistically significant level (from 4.47 to 4.76, p = 0.33).
- There was no statistically significant difference between AI-aided generalists and specialists.
- Generalists became more efficient at cancer detection, as evidenced by 15% improvement in their positive predictive value (from 3.38% to 3.89%).
- Although generalists’ recall rates did increase with AI (from 9.06% to 10.4%).
The Takeaway
The new study offers an intriguing look at how AI can be integrated into U.S. breast screening programs without dramatically disturbing workflow. It also shows how diagnostic performance can be improved in an environment where general radiologists are being asked to fly solo in an area they didn’t train in.

