AI tools enhance breast cancer recurrence prediction and radiopharmaceutical design

Researchers at New York University have created an artificial‑intelligence system that analyzes routinely prepared tumor‑slide images together with basic clinical data to predict the risk of breast‑cancer recurrence. In tests on more than 3,500 patients from seven countries, the AI matched or exceeded the performance of standard genomic assays, offering faster, cheaper results that could guide treatment decisions.

Separately, deep‑learning and generative‑AI techniques are being applied to radiopharmaceutical development. Machine‑learning models can identify promising drug candidates, forecast biodistribution, and generate patient‑specific digital twins to optimise radiation dosing. These advances promise more precise, personalised cancer therapy, though broader clinical adoption is limited by the need for high‑quality, standardized data sets.