- In January 2025, Roche received expanded FDA 510(k) clearance for its VENTANA DP 600 digital pathology slide scanner, extending the use of its digital pathology solution in clinical settings. This move demonstrates Roche's ongoing commitment to advancing regulatory-compliant whole slide imaging systems that support precise, AI-assisted diagnostics and seamless workflow integration for pathology labs across the U.S.
- In March 2025, Proscia showcased interoperability capabilities of its Concentriq platform at the DICOM WG26 WSI Connectathon, enabling standards-based integration with multiple scanners and image management systems. This development reinforces Proscia’s position as a leader in AI-powered digital pathology, improving flexibility and cross-platform accessibility in whole slide imaging environments
- In December 2024, PathAI launched PathExplore Fibrosis, an AI-powered solution designed for the quantitative analysis of fibrosis, collagen, and fibers directly from H&E-stained whole slide images. Integrated into its AISight platform, this release emphasizes PathAI’s focus on augmenting disease characterization and diagnostic workflows using intelligent digital tools, particularly in liver and kidney pathology
- In November 2024, Pramana, Inc. partnered with ARUP Laboratories to digitize large-scale pathology archives and develop AI-based hematopathology algorithms. This collaboration marks a significant step toward scalable, edge-AI deployment in whole slide imaging, enabling real-time diagnostic support and improving access to digital pathology services in clinical and research settings
- In October 2024, the U.S. agency ARPA-H launched the IMaging Data EXchange (INDEX) initiative to create a comprehensive platform for sharing high-quality medical imaging data—including whole slide images—to accelerate AI tool development. This initiative highlights the growing importance of whole slide imaging in AI-driven precision diagnostics and underscores government efforts to foster interoperability and data accessibility in digital pathology
