Researchers have developed a novel AI model that can analyze a routine whole histopathology image and simultaneously predict cancer subtype, specific genetic mutations, and survival outcomes across 32 different solid cancers rather than focusing on a single cancer type. The findings from the study in The American Journal of Pathology, published by Elsevier, highlight the potential of computational pathology to connect routine diagnostic imaging with molecular oncology.

Two recent scientific publications from the Fraunhofer Institute for Applied Solid State Physics IAF shed light on how the concept of quantum advantage can be assessed more precisely and realistically in the future. Both papers provide theoretical tools to make claims about quantum advantage more robust. Their content is complementary: one addresses quantum chemistry beyond idealized system models, while the other examines how algorithmic advantages can be reliably demonstrated through the scaling of problem sizes.