
AI predicts future lab results from patient records
A new transformer model can simulate how a patient’s lab values will react to specific drugs before they are even prescribed.
Discover the newest research about AI innovations in 👤 Personalized Medicine.

A new transformer model can simulate how a patient’s lab values will react to specific drugs before they are even prescribed.

The FDA is letting drugmakers recycle data from past trials to speed up gene therapies, but this regulatory shortcut places a massive burden of proof on developers.

A new self-supervised AI model accurately flags which complex, multi-illness patients are most likely to end up back in the hospital.

Regulators are forcing a public reckoning over whether marginal cancer screening benefits justify the risks of widespread adoption.

Static blood tests are failing HIV patients who seem healthy on paper but remain at risk of silent immune failure.

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.

Predictive algorithms can now spot type 2 diabetes risk ten years in advance, but the real bottleneck is how healthcare systems will handle millions of newly flagged patients.

A patient’s fate is often hidden in the messy paragraphs of their medical charts rather than their official disease stage.

A new clinical deployment in California is putting AI-driven embryo selection to its first real-world test.