Utilizes a proprietary Generalizable Pathology Foundation Model (GPFM) pre-trained on over 100,000 Whole Slide Images (WSIs). This self-supervised learning approach ensures robust, high-fidelity feature extraction superior to standard supervised methods Features the mSTAR model, a breakthrough in multi-modal learning that integrates pathology images with clinical reports and genomics data. This “knowledge-enhanced” approach allows the model to understand complex visual patterns Expanded training on over 500,000 WSIs to develop specialized foundation models for high-incidence cancers, including Lung, Breast, Gastric, and Colorectal Cancer (CRC). This ensures exceptional performance across diverse downstream clinical tasks A high-performance, scalable software infrastructure designed for real-world application. It seamlessly integrates all AI models into hospital workflows, supporting rapid inference, visualization, and interoperability with existing LIS/PACS systems