Discover the newest research about AI innovations in 👤 Personalized Medicine.
A new machine learning model predicts 30-day mortality for brain bleed patients using routine clinical data instead of expensive brain scans, challenging the assumption that advanced imaging is required for accurate prognosis.
A new model bypasses complex clinical charts to predict long-term death risk using nothing but raw billing codes.
A new AI-driven blood test could soon keep thousands of women out of the imaging room by ruling out womb cancer with near-perfect accuracy.
Static bone density scans miss how a patient’s body changes over time, but a new deep learning model proves that tracking those physical trajectories can prevent missed fracture risks.
Using virtual clones to simulate disease progression could finally solve the recruitment bottleneck that stalls rare disease drug development.
A new machine learning model uses basic blood markers to predict how kidney cancer patients respond to immunotherapy.
A new digital twin model shows that one-third of heart failure patients fail cardiac therapy because surgeons are aiming at the wrong target.
By combining two different genomic signals, researchers proved that cheap, shallow DNA sequencing can catch ovarian cancer with high accuracy.
A new benchmark shows that expensive tabular foundation models offer almost no performance advantage over classic machine learning for predicting patient outcomes.
An AI model that calculates biological age from a simple eye photo could shift systemic disease screening from specialized clinics to the optometrist’s chair.
