Zero raw data movement. Continuous model improvement. Privacy by design.
Medow is built around a federated loop: hospitals learn locally, encrypted updates are shared centrally, and stronger models return back to the edge.
A four-part loop that preserves local control while creating network-wide intelligence.
The technical architecture is designed so hospitals can contribute to collective model improvement without centralizing clinical records.
Local data is ingested
Hospitals ingest imaging, patient records, and clinical context using HL7 / FHIR-compatible workflows. All of it stays on-site.
Models train at the edge
On-site infrastructure learns from private hospital data locally instead of uploading records to a centralized AI service.
Only encrypted lessons travel
Encrypted model updates move across the network. Raw patient data never leaves the hospital premises.
The Medow Engine aggregates updates
Global model aggregation combines learning from participating hospitals and redeploys a stronger model back to every site.
Clinical validation is not an afterthought. It is part of the learning loop.
Clinicians review, validate, and correct AI outputs. That feedback becomes a high-quality proprietary improvement signal for future models.
The network flywheel compounds with every new partner.
More hospitals create more diverse local learning, which creates stronger models, which produces better outcomes and makes the network more attractive to the next partner.
Regulatory pathway
Medow is designed for CDSCO Software as Medical Device (SaMD) certification. Our evidence framework, explainability layer, and audit-oriented workflow design are intended to support certification work in parallel with pilot validation.
Learn more at cdsco.gov.in.
Explore the platform or talk with us about a privacy-first pilot.
If you want to see the loop in action, the next step is either the platform demos or a direct conversation about how Medow fits your hospital environment.