Rad AI is a healthcare technology firm that is utilizing AI to bring about revolutionary changes in the radiology department with its generative AI-powered solutions. By the confluence of medical know-how and cutting-edge technology, it is able to facilitate the processes being carried out, lessen the burden on doctors and patients, uplift medical attention, and thus, enable radiologists to work more quickly, intelligently, and accurately.
Rad AI is at the forefront of healthcare innovation by incorporating sophisticated artificial intelligence into radiology which boosts clinical workflows' accuracy and efficiency. Complicated reporting duties are made easier so that more attention could be given to important medical insights and patient care.
The company is the one that gets accolades from the leading organizations in the industry and is also the one that major medical institutions have faith in. Its method increases efficiency, cuts down on staff turnover, and builds a more intelligent, faster, and more human-centric healthcare environment in radiology alongside the teams.
| Legal Name | Rad AI, Inc. |
| Headquarters | San Francisco, California, United States |
| Business Model | B2B |
| Founding Date | February 2018 |
| No. of Employees | Approx 189 (As of 2025) |
Rad AI offers Rad AI Reporting, Rad AI Impressions, and Rad AI Continuity, designed to streamline radiology workflows, enhance productivity, and support efficient, accurate, and timely patient care.
| Jeremy Barnett | Chief Executive Officer & Co-Founder |
| Bradley Silver | President & Co-Founder |
| Emily Duban | Chief Revenue Officer |
| John Duban | Head of Strategy |
| Seth Weinkranz | Senior Director of Client Success |
| Maria Brown | Head of Communications |
| Hassan Zia | Senior Full Stack Developer |
| Emilian Tita | Lead Engineer & DevOps |
| Dusan Popovic | Creative Director |
| Joseph Freedman | Chairman |
Rad AI generates revenue through an enterprise SaaS model, leveraging subscriptions, strategic partnerships, and opportunities in radiology automation and data-driven intelligence commercialization.
Client Segments: Healthcare institutions, radiology groups, and medical networks seeking AI-driven efficiency and workflow optimization.
Target Companies: Radiology practices, hospitals, and health systems aiming to reduce physician burnout and improve patient outcomes.
Target Geography: Primarily North America, with growing adoption across Europe and selective expansion in Asia-Pacific.