Oxford spinout RADiCAIT uses AI to make diagnostic imaging more affordable and accessible - catch it at TechCrunch Disrupt 2025 | TechCrunch

"It starts with fasting for four to six hours before coming into the hospital - and good luck to you if you live rurally and your local hospital doesn't have a PET scanner. When you get to the hospital, you're injected with radioactive material, after which you must wait an hour while it washes through your body. Next, you enter the PET scanner and have to attempt to lie still for 30 minutes while radiologists acquire the image."

"Another bottleneck? 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 in rural and regional hospitals. But what if you could use AI to convert CT scans, which are much more accessible and affordable, into PET scans? That's the pitch of RADiCAIT, an Oxford spinout that came out of stealth this month with $1.7 million in pre-seed financing."

""What we really do is we took the most constrained, complex, and costly medical imaging solution in radiology, and we supplanted it with what is the most accessible, simple and affordable, which is CT," Sean Walsh, RADiCAIT's CEO, told TechCrunch. RADiCAIT's secret sauce is its foundational model - a generative deep neural network invented in 2021 at the University of Oxford by a team led by the startup's co-founder and chief medical information officer, Regent Lee."

PET scans require fasting, travel to equipped centers, injection of radioactive tracers, an uptake wait, and 30 minutes of stillness during imaging, followed by isolation from vulnerable people for up to 12 hours. Radioactive tracers must be produced in nearby cyclotrons and used within hours, concentrating PET capability in major cities and limiting rural access. RADiCAIT aims to convert widely available CT scans into PET-equivalent images using a generative deep neural network. RADiCAIT is an Oxford spinout based in Boston with $1.7 million pre-seed funding, a Top 20 Disrupt finalist, an open $5 million raise, and a foundational model developed in 2021 at Oxford.

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