Rising attendance in mammography screening programmes attest to the fact that women understand the importance of early breast cancer detection. However, the resulting workload increase is a growing challenge for many women’s imaging centres. At the 2025 SBI (Society of Breast Imaging) Symposium, breast imaging specialist Stamatia V. Destounis, MD, discussed her practice’s coping strategies.
Using AI to help detect one of the leading killers of women worldwide: A new machine learning model can successfully predict heart disease risk in women by analysing mammograms.
AI may be effective at detecting advanced breast cancer in mammograms, but current models still lack reliability. New research from Korea suggests that up to 14% of invasive cases are missed.
Mammography image interpretation AI models are unreliable – but so are human readers. A new hybrid strategy could reduce radiologist workload by 38% without compromising diagnostic efficacy.
More clinically relevant tumors detected at an earlier stage and at lower costs: New research finds that AI can replace the second radiologist in the Dutch breast cancer screening program.
Dense calcifications are common in breast tissue, but not every finding in a mammogram is a precursor of cancer. New insights could lead to fewer benign biopsies and guide therapeutic development.
By directing radiologists' attention to potentially suspicious areas, AI can help spot more lesions indicative of breast cancer in mammograms. This is suggested by a new study.
Using radiomics on mammograms of over 30,000 women, researchers have identified six breast texture patterns that may be associated with increased cancer risk.
A novel deep-learning approach automatically finds and classifies microcalcifications found in mammography images—bringing both accuracy and consistency to breast-cancer screening.
Despite their improving diagnostic accuracy, medical AI systems are often met with skepticism by radiologists. Their patients, however, seem more inclined to embrace this technology, a study shows.
3 out of 10: Mammography screening has been shown to be effective in reducing breast cancer mortality. Still, many cancers are diagnosed between screening rounds, a new study shows.
New findings show how AI-assisted mammography may not only reveal breast cancer, but can also assess calcium buildup in the arteries within breast tissue—an indicator of cardiovascular health.
AI-supported screening for breast cancer can detect more cancer cases compared with traditional screening, and find more invasive cancers at an early stage, results from the MASAI trial indicate.
Improved breast cancer detection, reduced workload for radiologists: The world’s largest prospective study on AI in Germany’s Mammography Screening Program reveals the technology's benefits.
Minimally invasive surgical interventions, innovative imaging and the use of AI: At the upcoming EUSOBI congress in Lisbon, experts present and discuss the latest advances in breast imaging. We spoke with Tanja Brycker from Hologic ahead of the event about new trends in women’s health, the company’s investment in innovation and education, and what the future of mammography looks like with the…
Measuring the calcium build up in the arteries of the breast, researchers have developed an AI-generated score for predicting cardiovascular disease in women from their mammograms.
Mammography can lead to “false positives,” meaning they do not result in a breast cancer diagnosis. This may discourage some women from future screenings, according to a new study.
Interdisciplinary collaboration between gynaecologists, radiologists, pathologists and breast care nurses following a palpation finding makes a decisive contribution to the success of further breast cancer treatment. This was the consensus among the speakers at the Annual Congress of the German Society of Senology in Dresden. The experts provided clear explanations of which imaging is best for…
Using artificial intelligence (AI), breast radiologists in Denmark have improved breast cancer screening performance and reduced the rate of false-positive findings.
