Researchers are developing smarter tools for pathology-driven decision support, aiming to reduce manual interpretive burden in oncology. A new AI system reads whole pathology slides and can answer clinician questions across 31 cancer types, with the stated goal of improving access to pathology insights and supporting faster, more consistent analyses. The work leverages an AI architecture trained on slide-level information so it can respond to natural-language queries, a step toward “interactive” pathology rather than static image classification. For clinical operations, the main value is reducing the time between question formulation (e.g., tumor features or marker context) and decision-relevant answers. If performance and safety hold up across sites, the approach could become a building block for integrated pathology workflows in routine cancer centers.