
AI finds protein markers for rare cervical cancer
A new AI tool bypasses the limits of scarce patient data to identify a two-protein signature that accurately flags a deadly, non-HPV cervical cancer.
Discover the newest research about AI innovations in 🤖 Machine Learning.

A new AI tool bypasses the limits of scarce patient data to identify a two-protein signature that accurately flags a deadly, non-HPV cervical cancer.

While doctors usually dominate the healthcare AI conversation, the real battle against clinical burnout is being won at the nursing bedside.

A new deep learning model outperforms traditional risk scores by extracting hidden risk signals directly from routine screening ultrasound images.

By rebuilding proteins backward, drug developers are trying to bypass the human digestive system’s natural defenses.

A new open-source AI model successfully automates the tedious manual tracking of heart chambers and blood flow velocity, moving cardiac imaging past simple ventricular checks.

Most clinical AI models fail when they leave their home hospital, but a new 26-model suite proves that reproducible clinical tools can survive the transition to different healthcare systems.

Algorithms can now recommend insulin doses as safely as human specialists, shifting the bottleneck of diabetes care from clinical expertise to patient trust.

Regulators are clearing the path for AI that writes its own clinical notes, shifting the technology from a simple second pair of eyes to an active administrative partner.

Medical AI models are passing clinical tests by reading machine settings instead of actual patient disease.

High-tech cancer diagnostics are currently a luxury of the wealthiest health systems, but a shift in how we analyze basic tissue slides could soon level the playing field.