
AI predicts survival times for rare brain disease
A new machine learning model predicts individual survival times for multiple system atrophy, forcing clinicians to rethink how they deliver terminal prognoses.
Discover the newest research about AI innovations in 🤖 Machine Learning.

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

The race to dominate clinical AI is no longer about transcribing doctor-patient chats; it is about controlling the entire administrative backbone of healthcare.

A massive clinical trial is putting machine learning in charge of life-or-death oxygen decisions for twenty-four thousand critically ill patients.

Automating the search for mucus plugs in lung scans reveals a hidden driver of COPD mortality that human eyes routinely miss.

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.