
AI brain monitoring predicts critical patient survival
A patient’s survival in the ICU may depend on how quickly clinicians can spot silent, ongoing seizures in the brain.
Discover the newest research about AI innovations in 🧠Neuroscience.

A patient’s survival in the ICU may depend on how quickly clinicians can spot silent, ongoing seizures in the brain.

A new algorithm can spot hyperacute stroke tissue changes on cheap, standard CT scans, but it struggles to map the exact boundaries of smaller lesions.

A newly cleared neuromodulation system shifts PTSD treatment from trial-and-error pharmacology to personalized brain mapping.

Tiny, involuntary eye movements captured without head restraints can train algorithms to flag early Parkinson’s disease.

A new deep learning model turns cheap, blurry brain scans into high-contrast maps, helping doctors spot stroke damage faster and agree on treatment.

A new study reveals that neurologists struggle to accurately predict stroke recovery because of systematic optimism and poor visual assessments, but AI models can correct these human errors.

A new machine learning model predicts individual survival times for multiple system atrophy, forcing clinicians to rethink how they deliver terminal prognoses.

A massive new study reveals that standard brain wave spikes only weakly correlate with actual seizure frequency, challenging how doctors monitor epilepsy.

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.

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.