The movement to address breast density in screening is not limited to Europe. Similar guidelines and practices are being considered and implemented in countries like Australia and New Zealand. The Royal Australian and New Zealand College of Radiologists (RANZCR) is also focusing on integrating AI and other technologies into breast cancer screening protocols, particularly for women with dense breasts.

Additionally, the Find It Early Act, introduced in Congress, aims to ensure that all women, regardless of insurance coverage, have access to breast cancer screening and diagnostic tests, enhancing early detection efforts and addressing disparities in access to care.


Trends and challenges in AI for mammography
Recent studies indicate that commercial AI algorithms can perform as well or above the level of human radiologists. The benefit of AI to breast cancer detection is particularly seen in clinical settings where radiology is experiencing significant backlogs and staff shortages.

In the U.S., where a single reader reads mammograms, radiologists are being tasked with reading increasing volumes without letting quality slip and missing cancers. Preliminary results of the AI-STREAM prospective study found that adding AI into radiologists' workflow increased the cancer detection rates of both General Radiologists and Breast Radiologists by 25% and 14%, respectively. The Breast Radiologists with AI had the highest cancer detection rates and lowest recall rates, providing an important quality safety net to radiologists in an increasingly high-pressure environment.

Another clinical setting facing growing pressures are European screening services that routinely use double reading; where two radiologists independently review each case, with a tiebreaker process if they disagree. With the global shortage of radiologists, maintaining this labor intensive protocol is leading to delays and strains on the screening service. . Recently, a major prospective study— the ScreenTrustCAD trial, conducted in Scandinavia— demonstrated AI’s potential to elevate this radiologist shortage. AI was used to replace one of the human readers, and the results showed that AI was non-inferior and, in some cases, even superior to having two human readers.

The success of these trials underscores the importance of conducting prospective studies under real-world conditions, where AI is used on actual patients within ongoing clinical practice. This approach contrasts with retrospective trials, which often utilize research datasets that may not represent the diversity of X-ray machines, population characteristics, and other variables encountered in clinical settings.

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