How can data sharing support AI in Life sciences and health

With the rise of AI & ML within Life Sciences & Health, it’s become obvious that a key blocker to success is not the maturity of the AI tools and techniques but access to data in sufficient volume and quality for the AI & ML methods to analyse. In this blog we discuss some of the options for data sharing and implications for AI/ML model building.

Depending on the AI/ML model being developed, having access to a broad cohort of data from across the domain will be critical to ensure the necessary diversity, edge cases and breadth that will make the analysis successful with broad applicability.

Other “softer”, less technical factors will also become increasingly important going forward, including the broader ethics of AI [1] and the possible regulatory implications of using AI for health decisions. The need to solve these issues will become greater as the potential & impact for AI in life science is shown and validated through the applicability of the models to decision making.

Data sharing approaches

All this raises key questions as to what types of data sharing approaches are needed to allow AI & ML to work successfully. A number of options exist for making data accessible and we discuss these below and provide examples of them.

Publish the data into a domain specific public research platform based on the key data types and usage (chemistry & assay data, pharmacological, imaging) that supports…