AI-powered radiology workflow to reduce burnout, save time, and improve patient care.
By Tanmay Verma, Founder · Last verified 03 Jul 2026
In short
Rad AI — AI-powered radiology workflow to reduce burnout, save time, and improve patient care. Best for Radiologists in high-volume practices seeking efficiency gains, Radiology groups looking to reduce burnout among staff, Health systems wanting to close the loop on follow-up recommendations. Contact Sales pricing.
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Rad AI is a purpose-built generative AI platform for radiology with strong traction among top US health systems. Its focus on reducing burnout and automating follow-up management addresses real pain points, though contact-only pricing and lack of public API limit transparency.
Last verified: July 2026
Across the latest 7 updates: 7 news mentions.
Blog article argues radiology reporting evolution extends beyond imaging, integrating broader clinical context.
Discusses responsible AI use in radiology, moving past replacement fears.
Critical look at past predictions that hindered radiology progress, implications for AI adoption.
Radiologist testimonial on how Rad AI Reporting improved patient focus.
SIIM 2026 recap: reporting's central role in radiology highlighted with Rad AI presence.
Blog on reducing information overload via in-workflow AI insights in radiology reporting.
Argues that traditional beep notifications should be replaced by invisible, reliable reporting software.
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
20 mentions across 2 sources (Hacker News, Lemmy).
How likely is Rad AI to still be operational in 12 months? Based on 4 signals — momentum (how recently it shipped), wrapper dependency, revenue model, and web presence.
Last calculated: July 2026
How we score →Rad AI offers a suite of AI-driven tools for radiologists and health systems, focusing on workflow automation. The core products are Rad AI Reporting, Rad AI Impressions, and Rad AI Continuity. Rad AI Reporting is a generative AI reporting platform that allows radiologists to dictate less while producing complete reports in their own language, reducing fatigue and improving accuracy. Rad AI Impressions automatically generates radiology impressions customized to each radiologist's style, saving over 60 minutes per shift. Rad AI Continuity automates patient follow-up for incidental findings, tracking more than 50 categories and significantly increasing follow-up rates. Rad AI is designed for radiologists, radiology practices, and health systems of all sizes, from small private groups to large integrated delivery networks. The company claims to work with over 40% of US health systems and 9 of the 10 largest US radiology practices. The AI models are trained specifically for radiology and healthcare, using one of the largest proprietary radiology report datasets in the world. The platform integrates seamlessly into existing radiology workflows, supporting both structured reporting and free dictation. Rad AI Continuity provides a safety net by automatically identifying incidental findings and managing follow-up recommendations, reducing liability and improving patient outcomes. The company was founded in 2018 by Dr. Jeff Chang, the youngest US radiologist in history, and Doktor Gurson, a serial entrepreneur. What sets Rad AI apart is its focus on generative AI for radiology, its deep integration with radiologist workflows, and its comprehensive follow-up management. Customer testimonials highlight improved efficiency, accuracy, and reduced burnout. Rad AI has raised significant funding, including a $60M Series C in 2025 led by Transformation Capital, and has been recognized by CB Insights, AuntMinnie, and Deloitte.
Rad AI is a purpose-built generative AI platform for radiology, not a general-purpose AI tool. Its three products—Reporting, Impressions, and Continuity—form a tight workflow for radiologists. The platform excels at reducing dictation fatigue and automating follow-ups, with over 40% of US health systems using it. However, it is expensive and requires vendor engagement for deployment. For radiology-specific needs, it outperforms general speech-to-text or NLP tools. But non-radiology specialties or organizations wanting a self-service AI tool should look elsewhere. The lack of published pricing and API is a barrier for smaller practices. Overall, Rad AI is a strong choice for radiology groups prioritizing burnout reduction and follow-up compliance.
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