
AI synthetic patients successfully replace real trial controls
A new study shows AI-generated synthetic patients can replicate cancer trial results, potentially shrinking the need for human control groups.
Discover the newest research about AI innovations in 🎗 Oncology.

A new study shows AI-generated synthetic patients can replicate cancer trial results, potentially shrinking the need for human control groups.

Averaging tumor data has long blinded oncologists to the specific, high-risk cells that drive patient mortality.

A new machine learning model uses simple blood and nutrition markers to predict which uterine cancer patients can safely skip aggressive surgery to preserve their fertility.

A new AI model turns the standard complete blood count into an instant classifier for leukemia and severe infections, bypassing the slow manual slide review that delays critical care.

Doubling scanning capacity is useless if there are no radiologists left to read the scans.

A new AI-driven blood test could soon keep thousands of women out of the imaging room by ruling out womb cancer with near-perfect accuracy.

A new regulatory shortcut could quietly solve the worst bottleneck in cancer treatment.

When algorithms read pathology reports better than the oncologists who ordered them, the bottleneck in cancer care shifts from diagnostic accuracy to human administrative capacity.

A new machine learning model uses basic blood markers to predict how kidney cancer patients respond to immunotherapy.

By combining two different genomic signals, researchers proved that cheap, shallow DNA sequencing can catch ovarian cancer with high accuracy.