External validation of a commercially available deep learning algorithm for fracture detection in children

Die Radiologie entwickelt sich von einer Dienstleistungssparte zur Echtzeit-Datenmaschine des Krankenhauses. Ein einziges Trauma-CT kann mehr als 2.000 Scheiben⁴ umfassen; in einem Tertiärzentrum fallen jährlich mehr als eine Million neuer Untersuchungen an. Gleichzeitig sehen sich Radiologen mit einer zunehmenden Komplexität und einem anhaltenden Druck auf die Belegschaft konfrontiert. Technologien, die lediglich den Durchsatz beschleunigen, reichen nicht mehr aus. Der Bereich braucht KI-Radiologie-Tools, die den klinischen Einblick verbessern und gleichzeitig das Wohlbefinden der Leser sicherstellen.

The U.S. Food and Drug Administration (FDA) has finished its first experiment with an artificial intelligence (AI) system that helps scientists review data, and it now plans to roll the technology out across every center by 30 June 2025. Healthcare leaders say the tool cuts hours of “busywork,” promises safer and faster approvals, and signals a new era in how regulators and industry will work together¹ ².

Radiology generates almost 90% of the data stored in a modern hospital¹, yet imaging volumes continue to rise faster than the global radiologist workforce can grow. Artificial intelligence in radiology, not merely an add‑on technology but a transformation in clinical culture, has emerged as the pragmatic answer. Modern AI radiology algorithms convert floods of pixel data into high‑fidelity insights in real time, allowing clinicians to move from image interpretation to patient‑impactful decision‑making. More than 340 imaging algorithms have received U.S. regulatory clearance (as of April 2025)², and a growing share of departments worldwide use at least one AI for radiology tools as part of routine practice³. These AI systems in radiology are best understood as cognitive extenders: they surface the invisible, triage the urgent, and automate the repetitive, while the radiologist retains ultimate clinical stewardship. In this brief, we explore why AI in radiology has become the strategic lever for imaging services, how AZmed positions itself within that shift, and what healthcare leaders should prioritise next.