Accelerate the creation and adoption of AI-based solutions in Healthcare

Healthcare AI is held back by the lack of access to clinically relevant data from a diverse patient population, collected in different locations and updated over time. Periodic deficiencies or insufficient volumes of data in some institutions. Privacy risk. Contrary to the privacy policies of some organizations and may make data more vulnerable to data breaches. Health disparities. Inequality of health, particularly in contexts with limited resources.

Innovative Approach

AI and Machine Learning

Machine Learning techniques are rapidly advancing technology, such as Automated Machine Learning. They are undoubtedly creating new opportunities and benefits for organizations from different sectors. However, the adoption of these new technologies involves third-party access to their ecosystems and corporate databases and is always accompanied by serious privacy concerns. Big companies are wondering how their data will be processed. This is an obstacle to the decision to outsource machine learning activities. This is why federated learning can often be a possible solution!

Federated Learning

Our approach is based on federated learning, which allows developers and researchers to collaborate between academic medical centers and researchers without ever moving data, transferring ownership or putting patient privacy at risk. Using a federated learning approach, Thauma eliminates the complexity, expense and risk of moving and managing huge volumes of data. An AI model is applied to the patient’s data in the place where she lives, and learning from each new data set informs the model’s refinement. Privacy first of all. Privacy always.

Our solutions allows healthcare AI developers and medical researchers to seamlessly access diverse and disparate pretrained models (pipelines) and use them to create better AI algorithms.

Why do we do it

Benefit of our solutions

Burndown is committed to creating equal access to advanced AI-based diagnostics and improved treatment pathways, for all patients. This requires prioritizing patient privacy, data diversity and reliable collaboration at every stage of the healthcare AI lifecycle. Federated learning makes it possible.