Research & Insights for Trustworthy Pathology AI

Pathology AI achieves human-level accuracy yet clinical adoption stalls. Hospitals lack frameworks to assess real-world performance beyond vendor benchmarks. Misaligned stakeholders—admins wanting ROI, pathologists fearing displacement, IT assessing infrastructure—can't build trust without evidence. Our tools expose AI's blind spots before deployment and predict real-world performance in your specific environment. Stop guessing where AI works or fails in your hospital—know exactly.

Label errors are a common yet overlooked source of brittleness in medical imaging and pathology. Systematic mislabels push models toward spurious patterns, hurting generalization and causing failures in production. Our SDK automatically spots these issues with representation analysis during training, flagging suspicious samples in the background with no extra work—saving expensive debugging cycles and valuable pathologist time.