Bridging the gap between raw medical data and high-confidence clinical decisions through rigorous AI validation and workflow integration.
Independent testing of AI algorithms against ground-truth subspecialty interpretation to ensure real-world reliability and safety.
Integrating AI findings into standardized reports that surgeons and cardiologists can trust and act on immediately.
Optimizing the human-machine interface to reduce diagnostic fatigue and eliminate interpretive errors.
AI models often perform differently in clinical practice compared to lab settings. We provide subspecialty-led validation for med-tech companies developing Cardiac CT and MRI tools, focusing on sensitivity, specificity, and the clinical nuance that automated systems often overlook.
We leverage AI for automated quantification—volumes, mass, and flow—allowing the radiologist to focus on complex tissue characterization and diagnostic synthesis. This results in faster turnaround times without compromising the depth of subspecialty review.