A study reported an AI model that can read pathology slides and answer clinician questions across 31 cancer types, aiming to streamline interpretation workflows that traditionally depend on time-intensive manual review. The work (Nature Cancer) involved collaboration between Shanghai Artificial Intelligence Laboratory and clinical partners including Shanghai General Hospital, Eastern Hepatobiliary Surgery Hospital, and Stanford University School of Medicine. Separately, a browser-based tool for de-arraying tissue microarrays emphasized auditable, FAIR-compliant digital pathology workflows to reduce preprocessing bottlenecks. Together, the updates signal faster path-from-image-to-decision pipelines and more standardized tooling for image-derived biomarker discovery.