Early disease detection saves lives

Detect any disease in a medical image with a single test using artificial intelligence.

Existing machine learning methods are too narrow. Our project can find all kinds of diseases with a single test.

Features illustration
Spot the Difference

Spot the Difference

By learning the distribution of healthy organ images using autoencoders, the model can mark a medical image as diseased if it is out of the distribution.

Precision Performance

Precision Performance

We use GANs to reconstruct a scan of a medical image. Diseased regions are identifiable since the reconstruction will be worse where the disease is most prevalent.

Detecting Any Disease

Detecting Any Disease

Our model studies healthy images to learn what makes them healthy, allowing it to spot any anomaly, including rare or new diseases.

Saving Doctors Time

Saving Doctors Time

A breast detection model will never detect pneumonia if it’s in an image. But with Zilic, we can. Rare disease detection will save doctors a ton of time, and prevent them from making errors in diagnosis.

What's Next

Get in touch if you can help us accomplish these goals.

Small Anomalies

Improving the detection of miniscule anomalies

We're pretty good at detecting medium to large sized anomalies in lung scans. It'll take some tweaking and experimenting to accurately predict super small irregulaties in every single organ.

Interactive Demo

Launching an interactive web demo

Before we get our model into healthcare facilities, we have to show that it works intuitively. Getting a demo ready will help generate support for our project.

Advisory Board

Growing our advisory board

We're looking for smart people who can help us impact as many lives as possible. Preferably medical professionals, experienced founders and ML veterans.

With next-gen disease detection using machine learning, it's like we're putting full-blown labs on laptops.