Rad AI

Generative AI for radiology: automates report generation and follow-up recommendations. Used by 40%+ of US health systems. $153M raised; $525M valuation. CNBC Disruptor 50.

Services HealthTech / Radiology AI / Clinical NLP
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San Francisco, CA, USA (Remote-Friendly) HQ
119 employees
2018 founded

What is Rad AI?

Rad AI builds generative AI that saves radiologists time and reduces burnout. Radiologists spend an enormous share of their day dictating and formatting reports, and Rad AI's products automate large parts of that: generating report impressions and full reports from findings, standardizing language, and surfacing follow-up recommendations so recommended imaging does not fall through the cracks. The company was founded in 2018 by Doktor Gurson (CEO) and Dr. Jeff Chang. Chang's background is unusual and central to the company's credibility: he started medical school at 16 and became the youngest radiologist in United States history, then spent about a decade working night shifts as an emergency-room radiologist, which is where the problem Rad AI solves became obvious to him. Rad AI is headquartered in San Francisco with roughly 119 employees, and its reach is substantial for a company that size: it works with more than 40% of all US health systems and 9 of the 10 largest US radiology practices. It has raised approximately $153M across four rounds, most recently a $72.9M Series C in January 2025 led by Transformation Capital with participation from health-system investors including Advocate Health, Memorial Hermann, Corewell Health, and Atlantic Health System, valuing the company at about $525M. It was named to the CNBC Disruptor 50 list in 2025. The health-system investor base is a meaningful signal: many of its backers are also its customers.

Mission & values

Save physicians time and reduce burnout by using generative AI to take over the repetitive parts of radiology reporting, so radiologists can focus on the medicine and patients get better follow-up.

Qualifications

Rad AI hires across machine learning and applied AI (clinical NLP, large language models, speech, evaluation), software engineering (backend, full-stack, infrastructure, integrations with PACS/RIS and EHR systems), data science, product, design, clinical and medical affairs (radiologists and clinical specialists who inform product and validate output), implementation and customer success for health-system deployments, sales, and G&A. Application flow lives at radai.com careers. The most relevant technical experience is production LLM/NLP work on clinical or otherwise messy domain text, plus healthcare-integration experience (HL7, FHIR, DICOM, PACS) for engineering roles. Because the product touches protected health information, HIPAA awareness is expected and background checks are standard. The company is remote-friendly with a San Francisco base, though remote eligibility varies by role, so confirm on each posting. Radiology or broader clinical domain knowledge is a strong differentiator even for non-clinical roles.

Leadership

D

Doktor Gurson

Co-Founder & Chief Executive Officer

Co-founded Rad AI in 2018 and leads it as CEO, taking the company from launch to working with more than 40% of US health systems.

J

Jeff Chang

Co-Founder

Co-founded Rad AI in 2018. Started medical school at 16 and became the youngest radiologist in US history, then spent roughly a decade as an emergency-room radiologist working night shifts, which shaped the product thesis.

Hiring process

  1. 1

    Apply at radai.com careers

    Browse open ML, engineering, clinical, product, and commercial roles and submit through the Rad AI careers page.

  2. 2

    Recruiter screen

    Confirms role fit, level, remote eligibility, and compensation expectations.

    About 7 days

  3. 3

    Hiring manager interview

    Role-specific conversation with the manager who owns the position and the clinical or technical context.

    About 7 days

  4. 4

    Technical or clinical loop

    ML and engineering: coding plus clinical-NLP / LLM evaluation discussion, often with a take-home. Clinical: radiology workflow and output-validation scenarios. GTM: health-system deployment case.

    About 14 days

  5. 5

    Values interview, offer, and background check

    Cross-functional interview, then an offer with base, bonus, and equity, plus the background check standard for handling protected health information.

    About 10 days

Funding

StageSeries C+
Total raised$153,000,000
Investors
Transformation Capital Advocate Health Memorial Hermann Corewell Health Atlantic Health System

Awards & recognition

  • CNBC Disruptor 50 · 2025

    CNBC

  • $72.9M Series C at a ~$525M valuation · 2025

    Rad AI

  • Used by 40%+ of US health systems · 2026

    Rad AI

Company information

Frequently asked questions

What does Rad AI do?
It builds generative AI for radiology that automates report generation (including impressions from findings), standardizes reporting language, and surfaces follow-up imaging recommendations so they are not missed. The goal is saving radiologist time and reducing burnout.
Who founded Rad AI?
Doktor Gurson (CEO) and Dr. Jeff Chang in 2018. Chang started medical school at 16, became the youngest radiologist in US history, and spent about a decade as an ER radiologist on night shifts.
How widely is it used?
It works with more than 40% of all US health systems and 9 of the 10 largest US radiology practices, which is unusual reach for a company of roughly 119 people.
How much has Rad AI raised?
About $153M across four rounds. The January 2025 Series C was $72.9M led by Transformation Capital, with health-system investors including Advocate Health, Memorial Hermann, Corewell Health, and Atlantic Health System, at a roughly $525M valuation.
Is Rad AI remote-friendly?
It is remote-friendly with a San Francisco base, but remote eligibility varies by role. Check each posting for the specific location requirement.
What experience does Rad AI look for?
For technical roles, production LLM and clinical-NLP work plus healthcare integration experience (HL7, FHIR, DICOM, PACS). Radiology or clinical domain knowledge is a strong differentiator even in non-clinical roles, and HIPAA awareness is expected.

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