Radiology AI Tools Dominate Latest FDA Device Approvals

The U.S. Food and Drug Administration has cleared dozens of new artificial intelligence-powered radiology tools, underscoring the specialty’s continued dominance in the field of medical AI.

In a recent update published during the first week of December, the FDA added 56 radiology-specific devices to its list of approved AI-enabled medical technologies. These additions bring the total number of AI tools designed for clinical imaging settings to 1,039—nearly 80% of all AI devices authorized by the agency to date. The full list now exceeds 1,300 AI-driven products, spanning specialties such as cardiology, neurology, pathology and beyond.

The approvals reflect a surge in development and adoption of AI tools in medical imaging. In early 2023, the FDA’s list included around 500 devices. That figure more than doubled by the end of the year and has continued to climb at a rapid pace. Among the latest approvals are tools from leading companies such as GE HealthCare, Siemens Medical Solutions, Fujifilm, Qure.ai and DeepHealth.

This momentum was on display at the Radiological Society of North America’s annual meeting, where over 200 AI vendors exhibited their latest technologies. More than half were featured in the AI Showcase, signaling a strong industry focus on developing smart solutions for imaging workflows.

AI’s expanding role in healthcare also appears to be gaining traction at the federal level. Days before the FDA’s update, President Trump issued an executive order aimed at reducing what he described as “excessive” state-level regulations that could stifle innovation in medical AI. The president warned that such policies risk undermining a “major growth engine” for the U.S. economy. Legal challenges to the order are expected, but it signals the administration’s clear intent to prioritize AI development.

With clinical imaging leading the charge, experts expect the pace of FDA approvals to remain strong heading into 2026. As new algorithms continue to demonstrate their ability to improve diagnostic accuracy, reduce radiologist workload and streamline patient care, radiology is likely to remain at the forefront of AI’s integration into everyday medicine.