
AI merges patient records and pathogen DNA
By combining complete electronic health records with bacterial genomes, a new AI platform predicts sepsis outcomes far better than traditional clinical scores.
Discover the newest research about AI innovations in 🧬 Genetics.

By combining complete electronic health records with bacterial genomes, a new AI platform predicts sepsis outcomes far better than traditional clinical scores.

The federal government is trying to force seven decades of incompatible medical research into a single format that artificial intelligence can actually understand.

An AI trained only on medical records managed to map the genetic drivers of human disease.

An algorithm trained on electronic health records can flag critically ill newborns who need rapid gene sequencing weeks faster than human doctors alone.

Measuring a single protein has been the gold standard for tracking ALS, but a new multi-protein signature suggests we have been missing the bigger biological picture.

A new nine-protein blood signature outperforms traditional clinical models and single-marker tests in predicting how fast ALS progresses.

A new deep learning tool bypasses the need for massive non-European genetic trials by adapting existing risk models to diverse populations.

Averaging tumor data has long blinded oncologists to the specific, high-risk cells that drive patient mortality.

A mismatch between the genetic makeup of blood donors and recipients is driving a silent crisis in sickle cell care.

Using virtual clones to simulate disease progression could finally solve the recruitment bottleneck that stalls rare disease drug development.