Rology, an FDA 510(k)-cleared AI-assisted teleradiology platform operating in the Middle East and Africa, has announced the successful closing of its latest growth funding round.
The round saw participation from major global organisations such as the Philips Foundation, Johnson & Johnson Impact Ventures, Sanofi Global Health Unit’s Impact Fund, and MIT Solve Innovation Future.
Their involvement reflects strong confidence in Rology’s goal of using AI and teleradiology to make radiology services more accessible, especially in remote areas and low- and middle-income communities.
“This round brings together mission-aligned leaders across industry, research, and impact investing to advance AI reporting and expand access to high-quality radiology across MEA,” said Amr Abodraiaa, CEO of Rology.
Rology’s platform offers AI-powered diagnostic reporting with zero setup costs, covering 12 radiology sub-specialties and 8 imaging modalities. It can deliver reports in as little as 30 minutes, making it fast and efficient for hospitals and clinics.
The company has delivered more than 1.3 million radiology reports and helped save over 1.2 million lives. With a network of over 200 radiologists serving more than 300 hospitals in 13 countries, Rology has become an essential part of healthcare systems across the Middle East and Africa, including in remote and underserved areas.
With radiologist shortages and rising imaging demand creating global bottlenecks, Rology is helping medical systems deliver faster, more accurate diagnostics. Its end-to-end teleradiology platform and AI-powered models provide high-quality reporting at scale.
Rology’s FDA 510(k)-cleared teleradiology system offers 99.89% clinical accuracy while reducing diagnostic reporting costs by up to 25%. This combination of precision and efficiency makes it a valuable solution for healthcare providers worldwide.
The latest funding comes after rapid expansion in Saudi Arabia and steady growth in Kenya and other markets. Rology has also launched eight AI tools designed for automated disease detection, early diagnosis, and workflow optimisation—using agentic AI, LLMs, and foundation models to support scalable, multimodal medical intelligence.
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