TesselAI offers an independent evaluation platform that quantifies how medical imaging AI models will perform in a specific hospital environment, providing stress‑tested, mechanistic insights into bias, reliability and failure modes. The service generates digital hypotheses of model behavior, integrates results with existing IMS/LIS workflows, and delivers reproducible reports that support compliance, reimbursement justification, and ROI analysis, all offered free to hospitals.
Funding
Funding not disclosed
Founders
Product
Problem
Hospitals struggle to adopt medical imaging AI because existing tools lack rigorous, real‑world validation, leading to uncertainty about safety, bias, and ROI. Without trustworthy evaluation and seamless integration into imaging and laboratory systems, pathologists and administrators cannot rely on AI outputs for clinical decisions.
Solution
TesselAI provides an independent, hypothesis‑driven evaluation platform that quantifies how a given AI model will perform in a specific hospital environment. The service builds a digital hypothesis of model behavior, identifying tissue structures, scanner variations, and bias sources that affect reliability. By conducting point‑in‑time, stress‑testing analyses and mechanistic interpretability, TesselAI delivers actionable insights that bridge the gap between research benchmarks and clinical reality. Results are delivered in a format that integrates with existing IMS/LIS workflows, enabling pathologists to trust AI recommendations and administrators to assess ROI. The platform is offered free to hospitals, removing financial barriers to rigorous AI assessment.
Target Audience
Primary customers are hospital pathology departments and imaging centers that need evidence of AI safety and performance before deployment, as well as AI vendors seeking validated clinical validation for their models.
Features
- Digital hypothesis generation that maps model behavior to specific tissue patterns and scanner characteristics
- Point‑in‑time stress testing across diverse data sources to expose bias, reliability gaps, and failure modes
- Mechanistic interpretability tools that highlight exact image regions driving model predictions
- Compatibility with hospital imaging (IMS) and laboratory (LIS) systems for seamless workflow integration
- Independent, reproducible evaluation reports that support compliance, reimbursement justification, and ROI analysis