
AI Detects Blood Cancers From Routine Blood Tests
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.
Discover the newest research about AI innovations in π Oncology.

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.

A new foundation model bypasses cherry-picked images to evaluate gastric cancer risk using every photo taken during an endoscopy.

Automating cancer registries with artificial intelligence sounds like an easy win, but new data shows these models fail at the precise timelines crucial for tracking patient care.

A new AI model predicts immunotherapy success across different cancers by translating complex genetic data into clear biological concepts.