AI in Cancer Detection: How Machine Learning Is Changing Breast Cancer Screening

Explore how AI and machine learning are advancing breast cancer screening from improving mammography interpretation to enabling emerging blood-based approaches.

Artificial intelligence (AI) is rapidly becoming part of the breast cancer screening landscape. For clinicians, however, the important question is not simply whether a technology uses AI. It is whether AI can generate clinically meaningful information that helps identify cancer, manage uncertainty, or inform the next step in care.

Recent evidence suggests AI has the potential to do just that. At the same time, important questions remain about validation, implementation, and how emerging AI-enabled technologies should complement established screening pathways.

From algorithms to clinical insight

AI broadly describes computer systems designed to perform tasks that traditionally require human intelligence. Machine learning (ML), a subset of AI, uses algorithms that learn patterns from large datasets and apply those patterns to new information.

In breast cancer detection, those inputs are increasingly diverse.

Some of today's most visible applications analyze mammographic images, identifying patterns associated with malignancy and helping radiologists prioritize or interpret findings. Other technologies under investigation are using AI to estimate future breast cancer risk from mammograms or help determine which patients may benefit from supplemental imaging.

And AI is moving beyond imaging altogether. Machine learning can analyze complex biological datasets, including thousands of proteins circulating in blood, to identify molecular patterns associated with cancer.

The common denominator is pattern recognition at a scale difficult to achieve through conventional analysis alone.

AI-assisted mammography is moving into clinical practice

AI-assisted mammography represents one of the most mature applications.

In a 2025 real-world German study involving 463,094 women, AI-supported screening was associated with a 17.6% higher breast cancer detection rate than screening without AI support—6.7 versus 5.7 cancers detected per 1,000 women.¹ Importantly, the higher detection rate did not come with an increase in recalls.

A separate 2025 prospective multicenter study of 24,543 women in South Korea reported a 13.8% increase in cancer detection when breast radiologists used AI-assisted computer-aided detection, again without a significant difference in recall rates.²

These findings demonstrate why AI is attracting attention: used alongside clinician expertise, algorithms may help surface subtle findings and improve screening performance.

Adoption is already occurring. STAT reported in 2025 that AI tools were being applied to millions of mammograms, including approximately 600,000 annually within RadNet's network alone.³

What AI has - and hasn't - solved

The evidence also calls for appropriate clinical perspective.

AI does not eliminate the limitations inherent to mammography. Dense breast tissue remains particularly relevant because both dense tissue and tumors appear radiopaque on mammography, potentially obscuring cancers.

A 2025 Radiology study examining 1,097 cancers found that an AI system missed 14%. Among the reasons investigators identified for AI-missed cancers, dense breasts were the most common.⁴

And despite growing adoption, questions remain about how AI affects patient outcomes across different populations and practice environments. In 2025, researchers announced the first large-scale randomized controlled U.S. trial evaluating AI in screening mammography, reflecting the need for additional prospective evidence within U.S. clinical practice.⁵

For HCPs, the takeaway is important: AI is another source of clinical intelligence, not a substitute for physician judgment, appropriate imaging, or evidence-based screening.

The next frontier: AI beyond the image

AI's role in breast cancer detection is also expanding from recognizing visual abnormalities to analyzing biological signals.

That distinction matters.

Imaging asks what can be seen anatomically. Proteomics examines proteins, or the dynamic molecular products and signals associated with biological processes. Machine learning makes it possible to evaluate thousands of these measurements simultaneously and identify complex patterns that could otherwise be difficult to interpret.

Astrin Biosciences is applying this approach through its deep-proteomics platform. Its platform profiles more than 9,000 proteins in a single run and uses AI to identify patterns associated with cancer.⁶

That technology underlies Certitude™, a blood-based test designed to support breast cancer detection, particularly for women with dense breasts or elevated risk. Certitude is intended to work alongside imaging rather than replace it, adding another layer of biological insight to the screening pathway.

For clinicians accustomed to choosing among mammography and supplemental imaging modalities, this represents a fundamentally different application of AI: instead of analyzing another image, machine learning helps interpret the molecular information contained in a blood sample.

What this evolution means for clinicians

The trajectory of AI in breast cancer detection is moving in several directions simultaneously from assisting radiologists with today's mammograms, to predicting future risk, to finding cancer-associated patterns in complex molecular datasets.

Yet sophistication alone does not establish clinical value.

For physicians managing patients with dense breasts, elevated risk, or uncertainty after mammography, the opportunity isn't to choose between established screening and AI. It is to understand where validated AI-enabled technologies can provide useful additional information.

AI is changing what clinicians can see in an image - and increasingly, what they can learn from biology.

Learn how Certitude and deep proteomics are bringing another layer of insight to breast cancer detection.