We're a healthcare AI company with a goal of creating a unified, polymathic AI system for solving the fragmentation problem in health AI. We're based in Ghaziabad, India.
The bottleneck in medical AI research isn't capability, instead it's the fragmented nature of currently deployed healthcare AI applications, designed for specific workflows and narrow use cases. This deployment fragmentation leads to a world where you have different AI systems for different imaging modalities, different diseases and for different populations.
We realized that breadth-oriented tools can solve the fragmentation problem in ways narrow ones never could.
Sentis - the broadest explanatory health AI tool built to understand major medical modalities - is our closest-yet expression of that insight.
Large DICOM studies are too long to review slice by slice. Sentis uses a mathematical filtering process to rank and surface the most relevant slices, reducing manual curation and focusing attention where the signal is strongest.
Health data is not static. Instead of stuffing every finding into context, Sentis can revisit prior scans and images and look for the patterns that matter when a follow-up question changes the task.
Sentis is not a single generalist model pretending to do everything. It acts as a control layer that coordinates specialist models so their outputs can be combined into one coherent result.
Jaiwardhan Tyagi
www.jaiwardhan.com.