
AI Software Extends Life of Hospital CT Scanners
Hospitals face a multi-million dollar dilemma: upgrade aging imaging hardware or accept degraded scan quality.
Discover the newest research about AI innovations in 🩻 Radiology.

Hospitals face a multi-million dollar dilemma: upgrade aging imaging hardware or accept degraded scan quality.

A new foundation model breaks open the black box of medical imaging by forcing AI to show its work through clinical concepts.

Deep learning models trained to spot Alzheimer’s disease are systematically blind to atypical forms of brain decay, raising doubts about their readiness for real-world clinics.

An aggressive regulatory gamble just paid off, positioning a major challenger to disrupt the established ultrasound market.

Hospital buyers assume all top-tier radiology AI performs the same, but new head-to-head data reveals critical trade-offs that could compromise patient care if ignored.

Automating the routine mechanics of ultrasound exams is no longer a luxury; it is becoming a survival strategy for short-staffed clinics.

Doubling scanning capacity is useless if there are no radiologists left to read the scans.

A new AI model drafts emergency head CT reports that radiologists cannot tell apart from human work.

A new regulatory shortcut could quietly solve the worst bottleneck in cancer treatment.

A new machine learning method extracts high-quality heart disease risk data from low-dose scans that doctors usually ignore for calcium scoring.