Discover the newest research about AI innovations in 🩻 Radiology.
A new human-in-the-loop training method proves that AI can slash the grueling hours radiologists spend labeling medical images without sacrificing clinical accuracy.
An autonomous AI agent just built a medical imaging tool that outperformed human-engineered models, proving that clinical-grade machine learning no longer requires a team of coders.
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.
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.
Automated translation of complex radiology scans can finally bypass the medical jargon barrier, but only if radiologists remain the ultimate gatekeepers.
A new deep learning model turns cheap, blurry brain scans into high-contrast maps, helping doctors spot stroke damage faster and agree on treatment.
Hospitals are drowning in imaging volumes, but buying new CT scanners to improve image quality is a financial non-starter for most struggling health systems.
A new wireless handheld AI system allows novice doctors to screen infants for hip dysplasia with expert-level accuracy.
A massive new study shows AI can spot cardiovascular danger on routine mammograms, but the technology faces a major reality check when compared to standard clinical risk calculators.
