We generated and released one of the first high-dimensional synthetic medical datasets for public access. This gives more people the opportunity to experiment with record-level cancer data and contribute to oncological research.

Training a machine learning model to signal patients who develop metachronous metastases after primary breast cancer, resulting in a more complete cancer registry.

By using the latest advancements in federated learning and secure computation, we can combine insights from data sources that are maintained by multiple organisations without the need to share record-level data.

Predicting the development of cervical cancer based on individual screening histories. This allows for faster intervention for individuals with higher risk, while reducing the number of examinations for individuals with lower risk.

A system to allocate the monthly cost of mobile phone consumption in a medical centre.