Intelligent Screening
for Pathology

Pathology is complex. Workflows shouldn’t be.

Workflow Acceleration

Designed to support more efficient review, SAINT can assist with case organization and highlighting cases for further evaluation. We help you focus your time on priority cases.

Evidence Based Recommendations

Created with expert input and validated with scientific rigor, SAINT provides clinical confidence and reduces diagnostic uncertainty.

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Interoperable & Accessible

Built for integration within existing Pathology PACS (IMS) and LIS environments. SAINT is designed to integrate within your existing infrastructure, so results are available within the system you use today.

INGESTION

Inputs from the LIS and WSIs make it possible to evaluate a case prospectively.

THE TRIGGER

Specimens, slides, stains and image file formats that meet our model criteria are automatically evaluated.

THE RESULTS

Cases and slides are classified as normal or not normal and can be prioritized and reported accordingly.

System Performance

The first release of our model boasts an industry leading 94.7% AUC. As new releases are available, new published performance results are available here.

new published performance results are available here

SAINT Data

Millions of images including over 200k cases with diagnostic supervision.

Santovia Path AI was founded on a simple belief: artificial intelligence, when responsibly developed and grounded in research, can advance digital pathology analysis.

Built upon real-world data from a large healthcare network, Prima CARE, and developed in collaboration with leading AI research institutions in the United States, the platform reflects a commitment to scientific rigor, responsible development, and practical application.

The goal is not simply to build algorithms, but to develop analytical tools that support pathology research workflows and data exploration.

Santovia Path AI is guided by a collaborative team of AI scientists, pathologists, and healthcare leaders working together to advance computational pathology.