Discover the newest research about AI innovations in 👤 Personalized Medicine.
A new language model attempts to solve a major diagnostic bias by separating normal hormonal transitions from actual viral damage in women.
A new machine learning model proves that tracking silent organ damage is far more predictive of survival than standard blood pressure readings.
A newly cleared neuromodulation system shifts PTSD treatment from trial-and-error pharmacology to personalized brain mapping.
An algorithm that spots diabetes risk a decade early could solve a massive bottleneck in preventive healthcare—if health systems actually know how to act on the data.
Moving diagnostic tools directly into primary care clinics can bypass the systemic barriers that historically leave minority patients underserved.
A new reanalysis reveals that highly praised machine learning survival models underperform both human doctors and a thirty-year-old statistical formula at predicting patient death at critical clinical milestones.
A new machine learning model predicts individual survival times for multiple system atrophy, forcing clinicians to rethink how they deliver terminal prognoses.
A massive clinical trial is putting machine learning in charge of life-or-death oxygen decisions for twenty-four thousand critically ill patients.
By ditching expensive gene sequencing for a simpler neural network and qPCR setup, researchers may have found a way to make early cancer screening practical for local clinics.
By translating messy medical jargon into mathematical vectors, a new tool could finally fix how language models understand patient charts.
