AI detects hidden breast cancers years before diagnosis in routine mammograms

AI model flags hidden breast cancers years before diagnosis in routine mammograms

By Hugo Francisco de SouzaReviewed by Susha Cheriyedath, M.Sc.Mar 9 2026 A large NHS screening study shows that artificial intelligence can detect subtle signals in "normal" mammograms that reveal which women are most likely to develop aggressive interval cancers years before they appear. Study: Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme. Image Credit: CameraCraft / Shutterstock In a recent study published in the journal npj Digital Medicine, researchers conducted a large-scale (n = 112,621) retrospective validation study to evaluate the performance of four state-of-the-art Deep Learning (DL) algorithms for predicting "interval cancers". These cancers account for approximately 30% of cancers diagnosed after a negative screening mammogram but before the next scheduled screening examination in screening programs and represent a critical diagnostic gap in current mammogram-based screening approaches. The study's findings revealed the academic DL model Mirai (developed by MIT) as the best-performing model (interval cancer AUC = 0.77). The model identified about 27.5% of interval cancers in the study cohort by flagging the top 4% of "normal" (negative) screening mammogram images as the highest risk. While the study noted that model performance varied slightly across the specific machines used to produce mammogram images and that one algorithm showed statistically significant differences between systems, these findings suggest that DL tools could potentially support risk-stratified breast cancer screening strategies, although prospective clinical evaluation would be required before implementation. Background: The Challenge of Interval Breast Cancers For decades, breast cancer screening recommendations have involved women receiving a mammogram once every few years (e.g., every 3 years in the United Kingdom [UK]). However, a growing body of evidence suggests that while these periodic screenings are necessary and effective at detecting most breast cancers, they fail to identify "interval cancers", cancers diagnosed after a negative screening mammogram but before the next scheduled screening. These "hidden" cancers, which are observed to develop or become clinically apparent in the periods between screening schedules, are often significantly more aggressive than those detected in routine mammograms, leading to worse prognosis and clinical outcomes, including death. Traditional approaches to addressing interval cancers have involved clinicians attempting to predict individual risk via genetic assessments (such as polygenic risk scores, which are not routinely implemented in most population screening programs) and family history evaluations (often incomplete). However, recent advances in Deep Learning (DL) algorithms have led researchers to hypothesize that these Artificial Intelligence (AI) models, trained on millions of mammogram images, may be able to recognize subtle imaging patterns and tissue characteristics in breast tissue that human radiologists might overlook. Unfortunately, given the wealth of commercial and academic DL models currently available, clinicians do not yet know which model to choose and whether these tools can perform well enough to be included in personalized care. Study Objective and Model Comparison The present study aimed to address this knowledge gap by conducting a head-to-head comparison of the breast cancer predictive performance of four of today's most advanced DL models: Mirai (MIT), iCAD ProFound AI Risk (a commercially available model), Transpara Risk (another commercially available DL tool), and Google Health's Risk Model. Validation Dataset From the UK NHS Screening Program These models were provided with an extensive retrospective validation dataset from the UK's National Health Service (NHS). The dataset comprised high-resolution "negative" (cancer-free) screening mammograms (n = 112,621) collected between 2014 and 2017 from two distinct NHS screening sites. Model performance was validated by tracking participants for five years to observe which women eventually developed breast cancers (approximately 1,225 cancers across the follow-up period), including interval cancers. Evaluation Across Mammography Hardware Platforms To evaluate the generalizability of algorithm performance across different mammography hardware platforms, DL models were trained on mammography images from different hardware ecosystems, specifically machines from Philips and GE. Predictive Performance of Deep Learning Models The study findings revealed that the academic algorithm Mirai consistently demonstrated the highest predictive power (Area Under the Curve [AUC] = 0.72; p < 0.001). While iCAD (AUC = 0.70), Google (AUC = 0.68), and Transpara (AUC = 0.65) achieved lower scores, their predictive performance was still notable given that the input mammograms had previously been interpreted as "normal" during routine screening. Identification of High-Risk Patients for Interval Cancers Study observations indicated that these models could identify future interval cancers from screening examinations initially interpreted as negative (Mirai's interval cancer AUC = 0.77). When researchers tested the top 4% of women identified by Mirai as being "highest risk," about 27.5% of all interval cancers in the cohort occurred within this high-risk group during follow-up. Expanding this high-risk group to the top 14% of women was observed to double the interval cancer detection yield, capturing approximately 50.3% of all future interval cancers in the cohort. Performance Across Mammography Machine Manufacturers The study also evaluated whether algorithm performance differed across mammography machine manufacturers. Researchers found that three of the four evaluated models performed statistically similarly on images generated by Philips and GE machines. While the Transpara model performed better on images generated by GE machines than on those generated by Philips machines, the difference was relatively modest (AUC = 0.69 versus 0.62). The researchers also highlight several limitations, including the exclusion of mammograms with implants or non-standard imaging views, incomplete ethnicity data, and the possibility that results may not fully generalize to mammography systems from other major vendors. The authors also note that retrospective validation may underestimate the potential clinical utility, since some cancers might be detected through additional imaging pathways rather than solely through symptomatic presentation. Conclusions: Toward Risk-Stratified Breast Cancer Screening The present study provides evidence suggesting that DL models can identify previously unrecognized imaging signals from standard mammograms to predict future cancer risk. Models such as MIT's Mirai were shown to identify and flag a significant proportion of interval cancers in a small group of high-risk women. Future work should aim to investigate these results in prospective clinical trials and real-world screening settings before such tools can be integrated into personalized screening protocols. Journal reference: Rothwell, J., et al. (2026). Performance of breast cancer risk prediction algorithms across mammography systems in the UK screening programme. npj Digital Medicine. DOI, 10.1038/s41746-026-02507-7, https://www.nature.com/articles/s41746-026-02507-7

Breast cancer detection 'up by 10% with use of AI' - study

Breast cancer detection can be improved by more than 10% with the use of an AI tool, according to the results of a new study. The evaluation was led by the University of Aberdeen following an NHS Grampian project. The team assessed how the AI software could be used to support healthcare workers in the routine breast screening of more than 10,000 women, who could also then be notified of the results more quickly. Yvonne Cook, from Aberdeen, who is in her 60s, had opted in to the AI research - and breast cancer was detected and then treated. "I just feel incredibly lucky," she said. The study's findings will now be expanded as part of a further trial looking at the use of AI in breast screening at sites throughout the UK. The AI tool, called Mia, has been developed by medical technology firm Kheiron. It can flag possible small and hard-to-spot areas of concern on mammogram scans that can be missed by the human eye. The breast cancer screening study, published in the Nature Cancer journal on Tuesday, found it could increase detection by 10.4%. It also found it could reduce staff workload, and cut the time to notify the women affected. The research team described the findings as "hugely significant" as earlier detection enables earlier treatment, and, in turn, a greater likelihood of treatment success. Yvonne went to what she thought would be a routine mammogram appointment in 2023. In the waiting room she noticed a sign explaining that a project was under way involving AI to assist in reviewing mammograms, and participation was optional. "It didn't occur to me for a minute to opt out," she said. "I think it said that AI would be utilised as part of the research project to review the mammogram and I just thought, why not?" A short time later, she received a recall letter requesting additional imaging. "I guess they don't want to alarm people unnecessarily, the letter said they wanted to do a follow-up mammogram which might be as a result of the initial result not being particularly clear. "When I arrived for that appointment, they said that it was the AI part of the analysis that had picked up something. "I had a scan and the consultant confirmed that the AI diagnosis was correct, that there was a small, Grade 2 tumour there, too small to be detected by the human eye." She added: "Overwhelmingly, I just felt incredibly lucky that I was part of the research programme and that it had been picked up at this early stage." Yvonne was immediately put on medication to inhibit the growth of the tumour, followed by surgery. "Had the AI not picked up the small tumour when it did, then either it would have been discovered at my next routine mammogram three years later, or I would have picked it up when it had grown to a stage that I was able to feel it," she said. "If that had been the scenario, then it's likely that the surgery would have been more invasive. "The cancer could have spread, it could have involved chemotherapy and a much longer recovery time with more impact on my life." Prof Gerald Lip, clinical director for breast screening in the north east of Scotland, said the results showed that AI could "effectively support" services by increasing cancer detection and reducing workload. "The bottom line here is without AI, doctors would not have caught these cancers as early," he said. "The translation of AI into clinical practice is one of the operational challenges in the coming decade. "Our findings will inform the conversation around using AI in healthcare."

How AI can improve breast cancer detection in the UK

Breast cancer affects one in every eight women in the UK. In this fight, early detection is crucial to giving people the best chance of overcoming the disease. New research from Google, Imperial College London and the UK's National Health Service (NHS), published as a pair of studies in Nature Cancer today, marks a turning point in screening technology and reveals how AI can strengthen early detection efforts. Our experimental research AI system identified 25% of the "interval cancers" that were previously missed -- the cases that typically slip through traditional screenings and only surface after symptoms appear, when they become more challenging to treat. But this research goes beyond the accuracy of the scans. It offers a first-of-its kind, large-scale look at how radiologists react when AI challenges or confirms their diagnosis in a clinical setting. In the UK's NHS, the frontline of breast cancer screening relies on a rigorous "double-reading" process: Two specialists must agree on every mammogram, with an arbitration panel deciding any disputes. It is a vital safety net, but one that's stretched to its limit. Each specialist must review roughly 5,000 scans annually, with just four hours of dedicated time per week, all amidst a global shortage of radiologists. We set out to determine how AI could help to tackle this challenge. The first step was comparing the accuracy of AI-based mammography interpretation to that of expert radiologists. We tested this by using AI to review the mammograms of 125,000 women, and the results were definitive: The AI-based screening detected 25% of the total interval cancers (cancers detected between scans) previously missed. AI also identified more invasive cancers and more cancers overall than the expert radiologists, and identified fewer false positives for women having their first-time scan.