Full-Time
AI-driven software for radiology workflows
No salary listed
Mid, Senior
No H1B Sponsorship
Remote in USA
Applicants must be legally authorized to work in the United States without employer sponsorship.
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Rad AI enhances radiology workflows using artificial intelligence to improve efficiency and accuracy in medical imaging. Its main product, Omni Reporting, automates routine tasks and ensures follow-up on incidental findings, making reporting more accurate for radiologists. Unlike competitors, Rad AI focuses specifically on integrating AI into existing radiology practices, providing a subscription-based model that allows for continuous software updates. The company's goal is to streamline healthcare processes while maintaining high standards of data security and patient privacy, as evidenced by its SOC 2 Type II and HIPAA certifications.
Company Size
51-200
Company Stage
Series C
Total Funding
$139M
Headquarters
San Francisco, California
Founded
2018
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Health Insurance
Health Savings Account/Flexible Spending Account
401(k) Retirement Plan
Paid Holidays
Remote Work Options
Unlimited Paid Time Off
Rad AI has closed a $60 million Series C funding round, led by Transformation Capital, valuing the company at $525 million. This investment will accelerate the development and deployment of Rad AI's generative AI technology for healthcare providers globally. Rad AI's solutions, including Rad AI Reporting and Rad AI Continuity, aim to improve radiologist efficiency, reduce burnout, and enhance patient care. The company has raised over $140 million to date and is expanding its team.
Rad AI, a healthcare startup founded in 2018, recently raised $60 million in a Series C funding round led by Transformation Capital, valuing the company at $525 million, as it continues to develop AI-powered tools to automate tasks for radiologists and reduce burnout.
Rad AI, a healthcare startup, raised a Series C funding round led by Transformation Capital, valuing the company at $525 million. The round brought in about $60 million. This follows a $50 million Series B led by Khosla Ventures in May 2024. Rad AI, founded in 2018, uses generative AI to automate tasks for radiologists. The company is working with nearly half of all US health systems and 9 of the 10 largest radiology practices.
Collaboration addresses longstanding inefficiencies in radiology workflow, as well as reducing burnout and empowering radiologists to focus on what truly matters - "staying in the flow". CHICAGO, Dec. 2, 2024 /PRNewswire/ -- AGFA HealthCare and Rad AI are announcing a strategic collaboration to enhance radiology workflows. Building on its leadership in Enterprise Imaging, AGFA HealthCare is raising the bar with Rad AI Reporting and FHIRcast integration. These cutting-edge solutions, powered by AI-driven reporting, enable radiologists to work faster, smarter, and with greater precision. By addressing critical workflow inefficiencies, AGFA HealthCare and Rad AI are not just setting the industry standard in data exchange and interoperability — they are shaping the future of radiology
AI-driven innovation increases radiograph efficiency by 48%, improves overall productivity by 25%, and alleviates radiologist fatigue. CHICAGO, Dec. 1, 2024 /PRNewswire/ -- Radiology Associates of North Texas (RANT), the largest private radiology practice in Texas and one of the largest in the nation, is transforming its operations with Rad AI Reporting, achieving a 48% increase in radiograph reporting efficiency and a 25% increase in overall hourly RVU productivity. These impressive results highlight Rad AI's longstanding leadership in generative AI. "It's incredible that Rad AI Reporting won AuntMinnie's 'Best New Radiology Software' recognition at last year's RSNA, and now, in 2024, we are already demonstrating transformational results as we deploy Rad AI Reporting to radiology groups nationwide," said Josh Duncan, Senior Vice President of Sales, Marketing, Customer Success. "The feedback and outcomes from RANT showcase why Rad AI Reporting is the fastest-commercialized software in radiology history."