How wearable health tech could help catch breast cancer

"Wearable health technology is expanding beyond smartwatches and fitness trackers -- it could one day detect and address conditions like breast cancer, according to MIT Media Arts and Sciences Associate Professor Dr. Canan Dagdeviren. Also: AI agents are transforming the healthcare and life sciences industry Dagdeviren specializes in flexible devices that can be laminated to areas of the body, whether that's skin, breast, or even the brain."

"These devices take biological signals and turn them into electrical signals for condition interpretation and analysis. The device is described as a wearable ultrasound patch that would screen for breast cancer outside of the doctor's office. She explains that the current standard of care for breast cancer screening is mammography, which she describes as a "painful" technology that doesn't work perfectly, especially for those with higher breast density."

"The device her team is developing, on the other hand, is radiation-free, non-invasive, and can detect anomalies in "subseconds," according to Dagdeviren. The most aggressive phenotype of cancer, interval breast cancer, develops between mammography screenings. This delayed diagnosis decreases the survival rate by 22%, Dagdeviren said. Also: Patients trust AI's medical advice over doctors - even when it's wrong, study finds The device collects lots of data through continuous wear and co-integrates with AI. These two factors allow it to estimate the progression of the anomaly or monitor how the condition is changing wi"

Flexible wearable devices can be laminated to areas of the body, including skin, breast, or brain, to convert biological signals into electrical signals for analysis. A wearable ultrasound patch designed for breast screening performs radiation-free, non-invasive detection of anomalies in subseconds. Mammography can be painful and less effective for people with higher breast density, and interval breast cancer that develops between screenings reduces survival rates by 22%. Continuous wear of the patch enables large-scale data collection and integration with AI. Combined data and AI can estimate anomaly progression and monitor changes in the condition over time.

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