45 Artificial intelligence may help radiologists identify subtle signs of breast cancer that are missed during routine mammograms, while some systems may also detect patterns associated with an increased risk of cancer before it becomes visible. However, significant clinical limitations remain, according to a review led by investigators at the UCLA Health Jonsson Comprehensive Cancer Center. The review, published in the Journal of Breast Imaging, examines commercially available AI tools used to assist with the interpretation of screening mammograms and assesses research into their potential to detect and predict interval breast cancers. Interval cancers are diagnosed after a negative screening mammogram but before a woman’s next scheduled screening. They are an important measure of how effectively a mammography screening programme is working and are often more aggressive than cancers detected through routine screening. “The ultimate goal of screening mammography is to eliminate these interval cancers and catch as many of them as we can earlier, at the point of screening,” said Tiffany Yu, MD, assistant professor of radiology at the David Geffen School of Medicine at UCLA and senior author of the paper. “As AI tools become increasingly commercially available, we wanted to provide readers with a timely overview and foundational understanding of interval cancers and potential ways AI can help detect them, because ensuring these tools are safe and clinically effective is paramount.” Some interval cancers develop rapidly and are not visible on the previous mammogram. Others leave subtle signs that were present on the earlier mammogram but were not recognised during screening. The researchers say these cancers could provide an opportunity for AI to help radiologists identify disease earlier. The review examined two main applications of AI. The first was retrospective cancer detection, in which researchers assessed whether AI could identify interval cancers that were retrospectively visible on an earlier mammogram. The second was future risk prediction, in which researchers assessed whether AI could identify subtle patterns on apparently normal mammograms that were associated with an increased risk of developing breast cancer later. Across retrospective studies, AI identified a substantial proportion of interval cancers that had subtle signs visible on the previous mammogram. Estimates ranged from about 5%–78%, depending on the study, AI system, and methodology. However, the researchers stress that these findings show potential for detection rather than proven reductions in interval cancer rates. Most studies were retrospective, meaning researchers examined previously collected mammograms from women who were later diagnosed with breast cancer to determine whether AI could have identified signs of the disease. “Finding a cancer retrospectively is very different from demonstrating that using AI during routine screening would have led to an earlier diagnosis,” said Hannah Milch, MD, associate professor of radiology at UCLA and co-author of the study. “We need prospective evidence showing that AI actually changes patient outcomes.” Some studies suggest AI may identify women at increased risk of developing breast cancer even when their mammogram appears normal. In one study, AI assigned its highest risk scores to 23.1% of women who later developed an interval cancer three screening rounds before diagnosis. That figure increased to 39.4% on the mammogram immediately preceding diagnosis. The findings raise the possibility that AI could provide an early warning signal before cancer becomes apparent. However, it remains unclear how clinicians should respond when AI indicates a high risk but no abnormality is visible on the mammogram. Additional imaging could potentially detect cancer earlier, but it could also lead to false positives, unnecessary procedures, and overdiagnosis. The review also highlights a large 2026 prospective randomised controlled trial involving more than 105,000 women. The trial compared AI-supported screening with standard double reading, a routine practice commonly used in Europe in which two radiologists interpret a single mammogram. The interval cancer rate was 1.55 per 1,000 women in the AI-supported group, compared with 1.76 per 1,000 in the standard screening group. AI-supported screening also had higher sensitivity, at 80.5% compared with 73.8%, while specificity was the same at 98.5%. The AI-supported approach reduced screening workload by 44.3% without reducing the overall cancer detection rate. The authors describe the trial as among the strongest prospective evidence to date for AI-assisted mammography. However, they caution that it was designed to assess non-inferiority rather than definitively demonstrate a reduction in interval cancer rates. The screening setting also differed from many US programmes, including the use of 2D mammography, longer screening intervals, and double reading. More broadly, the researchers found substantial variation among studies examining AI and interval cancers. Researchers use different definitions of interval cancer, as well as different screening technologies, screening intervals, and radiologist workflows. Most studies also assess a limited number of AI algorithms, making it difficult to determine whether findings from one system apply to others. “These differences make it difficult to directly compare results because of international screening programme differences, and the lack of studies applicable to US programmes,” said Yu. “This also highlights important areas where more research is needed before these tools can be fully integrated into everyday care.” The researchers say future studies should follow patients prospectively and determine whether AI-assisted screening reduces interval cancer rates, recall rates, false positives, radiologist workload, and downstream testing. Long-term patient outcomes and post-market surveillance of commercially available AI systems will also be important for assessing their safety and effectiveness. The study’s first author is Javier Galvan, MD, from the Department of Radiological Sciences at UCLA. The other author is Niki Nourmohammadi, DO, MPH, a radiology resident at Larkin Health System.