RRadical AI builds AI-powered software that augments radiologists by fitting into existing clinical workflows. Its core product acts as a clinical workflow orchestrator that triages medical imaging studies (such as CT scans and X-rays) by automatically identifying findings that indicate critical conditions and prioritizing those cases in the radiologist’s worklist, so urgent cases are reviewed first. The company targets hospitals and imaging centers with a B2B licensing or subscription model for its AI tools. What sets Radical AI apart is its emphasis on workflow optimization and diagnostic prioritization grounded in medical imaging expertise from its Duke-affiliated founders, aiming to reduce clinician burnout and shorten the time to diagnosis. The overarching goal is to alleviate the growing gap between imaging demand and the available diagnostic workforce while improving patient outcomes through faster, more efficient readings.
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Wiley partners with Radical AI to superpower materials scientists. High-quality data is a foundation for rigorous experimentation in the first AI-powered self-driving lab. Radical AI's self-driving labs are enabling a new speed of science, but doing so in deep collaboration with the institutions that shaped the field, from national laboratories to top academic and scholarly publishers. One such partnership is with the academic publisher Wiley. Radical AI references hundreds of thousands of papers every time it generates hypotheses for novel materials that are then synthesized and studied in the self-driving lab. A substantial portion of those are sourced via a collaboration with Wiley. Building the future - with strong foundations. As AI becomes more deeply integrated into scientific research, the quality and provenance of information have become increasingly important. Researchers need confidence that AI-generated outputs can be connected back to credible evidence and that recommendations are grounded in transparent, verifiable sources. Radical addresses this in two ways: First by referencing literature and experimental data, then by testing the most promising hypotheses in its self-driving lab. When a materials scientist inputs their target performance qualities, the long-horizon agent taps Wiley papers (as well as those from other academic publishers and open-source providers) for Retrieval-Augmented Generation (RAG), referencing relevant papers and documents to understand what has been done before and which novel compositions might be most promising. The system also references 70 million data points already generated from synthesis and characterization in the self-driving lab. Then, most crucially, the self-driving lab tests those AI-generated hypotheses through real synthesis and characterization. As candidate materials move through the lab, automated stations record data spanning composition, processing conditions, crystal structure, microstructure, density, hardness, stiffness, oxidation resistance, and more. This all feeds back into the AI to further fuel fast discoveries. Driving real results, faster. The process is working today: In initial campaigns aimed at discovering new materials to better serve the demands of hypersonic applications, Radical generated 300+ novel materials and outperformed the current industry-leading material in 16 weeks. As a point of comparison, the entire field has only experimentally studied 3,493 unique alloys in this space over 20+ years. See the case study Radical AI, Inc. published with Wiley here.
Radical AI's self-driving lab named one of TIME's Best Inventions of 2026. A look inside the TIME 2026 Best Invention supercharging scientists. Discovering a new material takes roughly 20 years. The bottleneck is human capacity: research moves only as fast as scientists can run experiments and learn from the results. Its self-driving lab removes that limit. How it works: Scientists set the target. Researchers enter the properties they need in a novel material. AI agents develop hypotheses. Antimatter, its AI engine, reads hundreds of thousands of research papers and screens billions of material compositions to find the most promising candidates. Robots run the experiments. Autonomous experimentation happens in a physical lab, around the clock. Robots build and test each candidate. Every result becomes data. The lab captures data from every experiment and uses it to inform the next step. Each cycle sharpens the one that follows. Novel materials in weeks, not decades. In August, Radical AI, Inc. announced that this approach produced a novel material in just 16 weeks. Testing by the Purdue Applied Research Institute confirmed it resists corrosion in extreme temperatures 125 times better than C103, a leading aerospace alloy widely used in rocket construction. Self-driving labs are transforming research and development across industries, and TIME chose ours as the one to feature on its Best Inventions of 2026 list. "Materials will be what accelerates the possibilities of new inventions, not what slows them down," co-founder and CEO Joseph F. Krause told TIME. Every breakthrough technology depends on materials. Rocket, reactor, and chip development all face the same dilemma: the materials they need do not yet exist. With a self-driving lab materials can now be designed and developed in tandem with the technologies they enable.
Sandia National Laboratories partners with Radical AI to research new materials for hydrogen purification. An AI-powered mission to diversify American energy sources and enable the next chapter of technological development. Millions of Americans stand to benefit from a diversified energy economy. Hydrogen power, which leaves just water as a byproduct, is an exciting site of invention in this pursuit. Sandia National Laboratories is partnering with Radical AI to perform research on metal-hydrogen interactions and discover new materials to separate hydrogen from waste gas streams. As a frequent collaborator with leading American technology companies, Sandia chose to do this research on Radical AI's platform because of its distinguished ability to bring together AI, lab automation, and comprehensive data analysis in a self-driving lab that reaches results fast. "There is a lot of work in high-throughput predictions, but the ability to drive robotic synthesis and feed those experimental findings back into our research is a big part of why we're so excited to partner with Radical on the discovery of new materials for hydrogen purification. Simulation only gets you so far; the experimental data is what we think will really accelerate the discovery of new materials," says Sandia Staff Scientist Vitalie Stavila. Why hydrogen purification will shape the future. Better hydrogen production, purification, and handling promises a new chapter of technological progress. Data center power, steelmaking, heavy-duty trucking, and shipping are all sectors racing to adopt these improvements, and the Department of Energy has set a public target of cutting clean hydrogen production costs to $1 per kilogram by 2031. Cheaper, more durable separation membranes are key drivers, since purification and handling are some of the biggest challenges in delivering hydrogen to a refinery, a fertilizer plant, or a truck stop. The challenge at hand. Both emerging production methods like geologic / natural hydrogen and existing pathways such as steam methane reforming and biological routes face a common problem: After the hydrogen is made, it must be separated from other gases. Palladium is the gold-standard material for membrane-based hydrogen separation, which allows for more efficient reactor designs than the pressure-swing methods used at most large plants today. But it's expensive. Traditionally, attempts to source alternatives have demanded years of work hand-fabricating alloy ribbons, coating them, and running each one through temperature and pressure tests that took between days and weeks per sample - if the sample didn't break entirely. And even successful attempts at using new materials for this longstanding problem have faced a challenging tradeoff: Materials that move hydrogen quickly tend to become brittle and break down quickly, while those that last longer are slower. What's ahead. Radical accelerates complicated research endeavors and validates findings with real experimental data in a self-driving lab. AI can pursue multiple material performance goals simultaneously by processing complex experimental data and cross referencing all previously published learnings from materials science. The system ultimately proposes the next best experiment more effectively than any human could. By coupling AI models with automated synthesis and characterization, the team is working toward faster iteration on membrane materials that hydrogen can penetrate while other gases remain behind. This partnership reflects a shared interest in charting a faster route to world-changing materials. As the work progresses, Sandia National Laboratories and Radical AI look forward to sharing the results and how these findings will shape new technology.
Radical AI: Branding the cutting edge of material science. * Services - Brand Positioning - Visual Expression - Verbal Expression - Digital Experience - Environmental Branding Radical AI is accelerating materials science R&D by combining generative AI, simulation, and self-driving robotic labs, reducing material discovery time from decades to months. One Design partnered with Radical AI to build a brand around its ambitious approach to advancing scientific discovery. Beginning with a foundational positioning strategy, One Design Company developed a visual identity inspired by the science itself - creating a flexible system that gives form to complex ideas while making the company's work feel clear, distinct, and accessible. One Design Company brought the brand to life across a new website and a comprehensive interior graphics program, creating a cohesive experience that extends from the digital world into Radical AI's physical space. Identity options are part of the creative process. On this project, the client couldn't decide between two concepts that rose to the top. The compromise? Use them both. The core wordmark is a typographic nod to the scientific concept of free radicals, while a secondary mark - used internally - speaks to the company's progressive vision of the future through custom, future-forward typography evocative of robotics. The Radical AI website was an early adopter of a contextual island navigation paradigm. Combined with immersive imagery and dynamic interactions, this approach resulted in a boundary-pushing digital experience that reflects the ambition and innovation of the brand. For Radical AI's new New York City headquarters, One Design Company developed an interior graphic program designed to bring the brand into the physical environment. Bold, immersive expressions of the identity transform the workplace into an experience intended to inspire, connect, and support a rapidly growing team. One Design Company is a research-driven design and development studio. For over a decade, One Design Company has explored the intersection of experience and technology - where powerful brands come to life. Want to learn more? Let's chat.
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Industries
Company Size
11-50
Company Stage
Seed
Total Funding
$55M
Headquarters
Israel
Founded
2024
Find jobs on Simplify and start your career today
We update Radical AI's jobs every few hours, so check again soon!Browse all jobs