
AI fails to predict pancreatic cancer drug choice
A highly anticipated multimodal AI outperformed other biomarkers at staging pancreatic cancer risk, yet it failed its primary test of matching the right drug to the right patient.
Discover the newest research about AI innovations in 🤖 Machine Learning.

A highly anticipated multimodal AI outperformed other biomarkers at staging pancreatic cancer risk, yet it failed its primary test of matching the right drug to the right patient.
Federal medical researchers are turning to artificial intelligence to survive an unprecedented deluge of scientific literature.

Low-cost portable MRI scanners can track multiple sclerosis progression, but only if we stop relying on algorithms built for high-end hospital machines.

A new training framework proves that medical AI can close the diagnostic gap for marginalized patients without sacrificing overall accuracy.

A massive chunk of mental health data is currently invisible to the insurers who pay for care.

A new multimodal AI model outperforms standard PET scan metrics to catch blocked coronary arteries before they cause a heart attack.

A new deep learning model proves that standard health checkups hold hidden, complex patterns that traditional risk calculators completely miss.

Venture capital is shifting focus from AI drug discovery to the expensive, slow-moving machinery of clinical trials.

A new analysis reveals how much of a brain-computer interface’s output comes from the user’s mind versus the AI’s predictive text.

Using AI to analyze free-text surgery names could finally make cheap observational health data as reliable as gold-standard clinical trials.