If you’ve ever had a PET scan, you know that it is a difficult test. The scan helps doctors detect cancer and track its spread, but the procedure itself is a nightmare for patients.
It starts with a four to six-hour fast before coming to the hospital – and if you live in a rural area and your local hospital doesn’t have a PET scanner, good luck to you. When you reach the hospital, you are injected with the radioactive substance, after which you have to wait for an hour for it to enter your body. Next, you enter the PET scanner and have to try to remain still for 30 minutes until the radiologist acquires the image. After that, you have to physically stay away from the elderly, young people, and pregnant women for 12 hours because you are literally semi-radioactive.
Another hurdle? PET scanners are concentrated in major cities because their radioactive tracers must be produced in nearby cyclotrons – compact nuclear machines – and used within hours, limiting access to rural and regional hospitals.
But what if you could use AI to transform CT scans, which are more accessible and affordable, into PET scans? This is the pitch from RADiCAIT, an Oxford spinout that quietly emerged this month with $1.7 million in pre-seed financing. The Boston-based startup, which is among the top 20 finalists in the Startup Battlefield at TechCrunch Disrupt 2025, has launched a $5 million raise to advance its clinical trials.
“What we really do is, we’ve taken the most limited, complex and expensive medical imaging solution in radiology, and we’ve replaced it with the most accessible, simple and affordable, which is CT,” RADiCAIT CEO Shawn Walsh told TechCrunch.
RADiCAIT’s secret sauce is its foundational model – a generative deep neural network invented at the University of Oxford in 2021 by a team led by the startup’s co-founder and Chief Medical Information Officer, Regent Lee.
The model learns by comparing CT and PET scans, mapping them, and picking out patterns in how they relate to each other. Sina Shandeh, RADICAIT’s chief technologist, describes it as linking “specific physical events” by translating physical structure into physiological function. The model is then directed to pay extra attention to specific features or aspects of the scan, such as certain types of tissue or abnormalities. This focused learning is repeated several times with many different examples, so that the model can identify which patterns are clinically significant.
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The final image that goes to doctors for review is created by combining multiple models working together. Shahandeh compares this approach to Google DeepMind’s AlphaFold, the AI that revolutionized protein structure prediction: Both systems learn to translate one type of biological information into another.
Walsh claims that the RADiCAIT team can mathematically prove that their synthetic or generated PET images are statistically identical to real chemical PET scans.
“What our trials show is that when a doctor, radiologist, or oncologist is given a chemical PET or (our AI-generated PET), the quality of the decision is similar,” he said.
RADiCAIT does not promise to replace the need for PET scans in specific therapeutic settings, such as radioligand therapy, which kills cancer cells. But for diagnostic, staging and monitoring purposes, RADiCAIT’s technology could make PET scans obsolete.
“Because it is such a limited system, there is not enough supply to meet the demand for diagnostics and theradiagnostics,” Walsh said, referring to a medical approach that combines diagnostic imaging (i.e., PET scans) with targeted therapy to treat diseases (i.e., cancer). “So what we’re looking to do is meet that demand on the diagnostic side. The PET scanners themselves should fill the gap on the theradiagnostic side.”
RADiCAIT has already begun a clinical pilot specifically for the lung cancer test with major health systems such as Mass General Brigham and UCSF Health. The startup is now conducting FDA clinical trials — a more expensive and involved process than what is driving RADiCAIT’s $5 million seed round. Once this is approved, the next step will be to conduct commercial pilots and demonstrate the commercial feasibility of the product. RADiCAIT wants to run the same process – clinical pilot, clinical trial, commercial pilot – for the colorectal and lymphoma use cases.
Shahnadeh said RADiCAIT’s approach to using AI to gain valid insights without the burden of difficult and expensive tests is “widely applicable.”
“We are exploring expansion into radiology,” Shahnadeh said. “Expect to see similar innovations connecting domains from materials science to biology, chemistry and physics where nature’s hidden relationships can be learned.”
IF YOU WANT TO HEAR MORE ABOUT RADICAIT Join us at Disrupt in San Francisco October 27-29. Learn more here.
