Computational Imaging & Pathology
Paige AI
by Paige.AI, Inc.
FDA-authorized AI pathology platform detecting cancers and guiding treatment from whole-slide images
Category
Computational Imaging & Pathology
Founded
2017
Headquarters
New York, NY, USA
Overview
Paige is a computational pathology company that develops AI-powered diagnostic software to assist pathologists in detecting, characterizing, and grading cancers from digital whole-slide images (WSI). The company's flagship product, Paige Prostate, received FDA De Novo authorization in 2021 as the first AI-based software tool for pathology — a landmark regulatory milestone for the field of computational pathology. Paige's AI models are trained on over one billion pathology images from Memorial Sloan Kettering Cancer Center (MSK), one of the world's largest and highest-quality annotated pathology datasets. Pathologists at academic medical centers, community hospitals, and reference labs use Paige to triage high-risk cases, detect cancer foci that might be missed by eye (particularly in high-volume prostate biopsy reading), and reduce diagnostic variability across sites. Clinical evidence shows Paige Prostate reduces false negatives by up to 70% compared to unassisted pathologists, capturing clinically significant cancers that would otherwise be missed. Paige has built a comprehensive suite beyond prostate, including models for breast, colon, lung, and hematology pathology, as well as biomarker prediction models (HER2, MSI, PD-L1 by image analysis) that can guide targeted therapy selection without additional assays. The company's partnership with Microsoft and integration with Philips IntelliSite and Leica digital pathology platforms positions Paige as the leading AI layer in enterprise digital pathology deployments.
Key Features
Multi-Cancer Detection
Pan-cancer screening algorithms detect multiple cancer types from tissue morphology.
Digital Slide Management
Cloud-based storage and management of digitized pathology slides with annotation tools.
Tumor Microenvironment Analysis
Characterize immune cell infiltration, spatial organization, and tumor-stroma interactions.
Continuous Learning
Models improve continuously from pathologist feedback and new diagnostic cases.
LIS Integration
Seamless integration with laboratory information systems for clinical workflow adoption.
Pros & Cons
Pros
- +AI-powered pathology analysis achieves pathologist-level accuracy for cancer detection and grading
- +Continuous learning from pathologist feedback improves model performance over time
- +Integration with laboratory information systems enables seamless clinical workflow adoption
- +FDA-cleared algorithms validate AI-assisted diagnosis for clinical deployment
- +Deep learning models identify morphological features predictive of treatment response
- +Multi-stain analysis quantifies biomarker expression across tissue microarrays automatically
- +Whole-slide image analysis processes hundreds of slides per hour versus manual review
Cons
- −Whole-slide image digitization requires expensive slide scanners and substantial storage infrastructure
- −Training data scarcity for rare diseases limits AI model development for niche applications
- −Pathologist adoption faces cultural resistance and workflow integration challenges
- −Regulatory approval for diagnostic AI requires extensive clinical validation studies
Use Cases
Research Workflow Optimization
AI-powered optimization of research workflows to accelerate discovery timelines and improve reproducibility.
Data Analysis & Insights
Machine learning analysis of complex biological datasets to extract actionable insights and identify patterns.
Collaboration & Knowledge Management
Platform-enabled collaboration across distributed research teams with integrated data sharing and knowledge capture.