New nature-published research built a breast cancer AI test that uses routine microscope slides and clinical data to rank recurrence risk correctly about 71% of the time.

A foundation AI model learned tissue patterns from 400M patches, then helped turn routine slides into recurrence-risk evidence.

Breast cancer care already uses tumor size, nodes, receptors, grade, and sometimes gene assays.

Those signals guide therapy, but they still miss hidden recurrence risk inside ordinary tissue structure.

This system adds the missing visual layer by reading digitized pathology slides alongside routine clinical data.

Kestrel (the pretrained image-reading engine they used inside their new breast cancer test) learned patterns from 400M pathology patches, then scored tumor morphology without hand labels.