by
Gus Iversen, Editor in Chief | March 06, 2026
RadNet has acquired the Paris-based radiology AI company Gleamer, integrating the business into its digital health subsidiary, DeepHealth.
Gleamer develops cloud-based AI tools used in medical imaging workflows, particularly in routine modalities such as X-ray. The company employs more than 130 staff and supports more than 700 customer contracts across 44 countries. Its product lineup includes FDA-cleared and CE-marked software covering musculoskeletal, breast, lung and neurologic imaging applications.
The acquisition is structured as an all-cash transaction valued at up to €230 million ($260 million), including milestone payments.
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According to RadNet, the addition of Gleamer’s technology extends DeepHealth’s existing AI portfolio, which includes solutions for breast, chest, neuro, prostate and thyroid imaging, and expands its global footprint in radiology software. The combined offerings now span major imaging modalities including MR, CT, X-ray, mammography and ultrasound.
Howard Berger, president and CEO of the Los Angeles-based imaging services provider, said the acquisition addresses rising imaging volumes and persistent shortages of radiologists. “As imaging volumes continue to rise amid an accelerating shortage of radiologists worldwide, reengineering high-volume workflows, particularly routine imaging such as X-ray, ultrasound and mammography, is becoming essential to sustaining access, efficiency and quality of care,” Berger said.
Founded in 2017, Gleamer has focused on AI systems designed to assist with detection, prioritization and reporting tasks in radiology. The company reported strong recurring revenue growth in recent years and operates a software-as-a-service model. RadNet expects Gleamer’s annual recurring revenue to reach roughly $30 million in 2026.
RadNet said it plans to deploy Gleamer’s workflow and AI tools across its imaging center network, which spans several U.S. states. The company expects the integration to support automated case prioritization and draft reporting, particularly in X-ray imaging, which represents about a quarter of RadNet’s exam volume.
