
AI reconstructs brain pressure without skull drilling
By predicting brain pressure from routine heart and blood signals, a new deep learning model challenges the necessity of invasive skull-drilling in intensive care units.
Discover the newest research about AI innovations in 🤖 Machine Learning.

By predicting brain pressure from routine heart and blood signals, a new deep learning model challenges the necessity of invasive skull-drilling in intensive care units.

Throwing millions at AI imaging algorithms will not solve the UK’s cancer crisis without the human staff to act on the results.

A new deep learning model proves that artificial intelligence does not just mimic old diagnostic rules to spot deadly heart valve disease—it finds hidden signals doctors have been missing for decades.

A new machine learning tool measures eye gaze and facial expressions during autism evaluations, but its struggle to distinguish autism from other developmental conditions reveals the limits of automated diagnostics.

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.

A new machine learning pipeline proves that algorithms can label millions of breathing mismatches without losing accuracy, bypassing the human expert bottleneck in intensive care.

A new machine learning workflow cuts the detection time for superbugs from days to sixty minutes, shifting the battle against drug-resistant hospital infections.

A new partnership model shows health systems how to stop giving away their most valuable data assets to tech giants.

Standard radiation therapy treats every brain tumor margin the same way, but deep learning reveals that tumor geometry and location dictate how cancer spreads.

Off-the-shelf automated machine learning can flag deadly hospital-acquired infections, but only if hospitals feed them the right clinical data.