Artificial intelligence and machine learning algorithms feature in much of our coverage on Health Tech Insider. AI has clearly gone past tipping points of acceptance and application in medical science. Teams from institutions worldwide now routinely build and refine algorithms based on large datasets. We have written about machine learning identifying suicidal adolescents, predicting pain levels from facial videos, uncovering previously hidden insights about diabetes, and much more.

OWKIN, a New York-based machine learning technology company, recently introduced the OWKIN Loop Network (OLN). The network includes more than 30 top hospitals and research institutions in Europe and the U.S. The shared goal of the OLN members is to enable researchers to train predictive models with massive amounts of shared, real-world data. OLN also assists in knowledge transfer to benefit patients and speed pharmaceutical research and development.

The OLN purview is intentionally broad, but its current areas of focus for advancing research and development include oncology, cardiovascular, neurodegenerative, and autoimmune diseases. Specific projects include training predictive models to identify quantitative biomarkers for a rare form of cancer, to predict brain age from MRIs, and to establish gene expression profiles as markers of immunotherapy response.

Pooling resources for practical applications of a rapidly developing technology such as machine learning — particularly when the mission focuses on open sourcing the wins — certainly sounds like a timely and broadly beneficial effort. It will be interesting to watch for similar networks to observe whether they are or become collaborative or competitive.