Vascentis provides an AI‑driven clinical decision support platform that combines genomics, biomarkers, imaging, and traditional risk scores to generate patient‑specific cardiovascular therapy recommendations. The system assesses eligibility for advanced treatments, suggests targeted diagnostic tests, and continuously refines its guidance through reinforcement learning based on real‑world outcomes, delivering concise, actionable insights at the point of care.
Funding
Funding not disclosed
Founders
Product
Problem
Cardiovascular disease remains the leading cause of death and disability worldwide, and clinicians often rely on generalized guidelines that do not account for individual biology, advanced imaging, or nuanced risk factors. This leads to underutilization of high‑value therapies, unnecessary testing, and higher rates of readmission and adverse events.
Solution
Vascentis offers an AI‑driven clinical decision support platform that integrates advanced imaging, biological markers, and standard risk factors to generate patient‑specific therapy recommendations. The system evaluates eligibility for advanced cardiovascular therapies and suggests targeted diagnostic tests to confirm the optimal treatment path. Recommendations are continuously refined through reinforcement learning that incorporates real‑world outcomes, improving decision quality over time. The platform is built to meet clinical standards, delivering clear, actionable insights that clinicians can trust at the point of care.
Target Audience
Primary users are cardiologists, cardiovascular specialists, and hospital care teams responsible for prescribing and managing advanced cardiovascular therapies.
Features
- Biology‑aware AI engine that combines genomics, biomarkers, imaging data, and conventional risk scores to produce individualized treatment suggestions
- Real‑time eligibility assessment for advanced cardiovascular therapies, including intensive lipid‑lowering, anti‑glycemic, anti‑inflammatory, and emerging agents
- Targeted test recommendation module that prioritizes diagnostics needed to validate the chosen therapeutic approach
- Reinforcement learning loop that updates the model based on observed patient outcomes, enhancing future recommendations
- Clinician‑focused interface delivering concise, actionable guidance that aligns with regulatory and hospital workflow requirements