Unlocking the Potential of AI and Federated Analytics in Healthcare

AI and Federated Analytics

AI in Life Science and Healthcare

Artificial Intelligence (AI) is expected to transform life science and healthcare by improving efficiency, personalizing treatments, enhancing diagnostic capabilities, and transforming the overall patient experience. These advancements have the potential to drive significant improvements in the quality, accessibility, and cost-effectiveness of healthcare services.

BC Platforms believes Federated AI and other privacy preserving data analytics techniques will become big in the near future, and we are equipped to support our clients using different modern data analysis tools.

Capabilities for applying AI

BC Platforms has developed software tools to facilitate the use of massive genetic databases, as well as different annotation databases for AI model training. In addition, we have a global data partner network equipped with clinical and different molecular data for AI model training and validation.

Our main interests for applying AI are:

Harmonizing unstructured healthcare data to structured format
Development of personalized medicine models and different biomarkers
Clinical trials using genetic information
Drug repurposing

Federated Data Analysis and AI

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Strict Data Security Requirements

Federation Techniques AI Model Training

Federated Data analysis Fits Perfectly with BC Platforms’ Data Network Architecture

Due to strict data security requirements for healthcare data, especially genetics data, many countries and continents have set various rules for protecting patient privacy. Federated data analysis, where individual level data never leaves the original data controller site – among other modern privacy preserving analysis techniques – are becoming increasingly popular.

Federation techniques can also be used for Artificial Intelligence (AI) model training where only AI model parameters move between the participating data controllers. Supported by many research articles, these techniques give very similar results as analyzing individual level data, but without compromising data privacy. To make federated analyses possible in practice, all used data must be harmonized to standard data format e.g. OMOP Common Data Model.

Federated data analysis, as well as other privacy preserving analysis techniques, fits perfectly with BC Platforms’ Data Network architecture where all the data sources are OMOP harmonized and connected to one Central Node (BC|RQUEST) which can orchestrate federated data analysis tasks. Hardware and Cloud agnostic technology facilitates running analyses in mixed environments where different data sources may use various different Cloud or on-prem computing environments.

Learn More about Data Federation and our Secure Technology Platform