What we’re not talking enough about when it comes to the recent Nobel Prizes in Chemistry and Physics….
Data access and data privacy.
This week has seen the recognition of some truly landmark progress.
But we’re not going to realize the full human health potential of machine learning and AI models unless we have access to real-world health datasets.
So much of this health data right now is “dark data.”
Just like dark matter in our universe, it’s abundant, but it can’t be seen, accessed, or used. It means that different communities of researchers, including those in the worlds of AI/ML, can’t build models off the data that could generate new diagnostics, biomarkers, or cures.
And there are important reasons for these obstacles.
The privacy of patient and participant data is absolutely paramount. Open science can only move forward in the context of safe and ethical data management and reuse.
We see that way forward. A way to illuminate these dark datasets while maintaining strict governance controls.
Things like synthetic replicas of human datasets that contain no sensitive personal information. And AI copilots that could help new communities of scientists explore the data.
At our recent on-site Home Week, Luca Foschini explains how these are the kinds of things we’re building at Sage.
This is how we accelerate ethical data reuse and use this new wave of technology to improve the lives of patients.
#AI #ML #NobelPrize #openscience #dataillumination
rare disease and cancer research
3moAlso, forgot to credit Anh Nguyet V. who played a key role in the workshop, my apologies!