PRISM2 is a novel multimodal foundation model designed to enhance the analysis of whole-slide images in computational pathology. It has been trained on an extensive dataset comprising 700,000 diagnostic specimen-report pairs, enabling it to integrate clinical dialogue with image data effectively. The model aims to improve clinical utility by providing more comprehensive and accurate interpretations of pathology slides. As a result, PRISM2 represents a significant advancement in the field of pathology AI, potentially unlocking new capabilities for multi-modal diagnostic support. Early assessments suggest that the model is effective in its intended application, reflecting positive prospects for its adoption in clinical settings. This development aligns with ongoing efforts to leverage artificial intelligence to augment medical diagnostics and improve patient outcomes.