RADiCAIT is a Boston-based spinout from the University of Oxford that employs a generative deep neural network to transform standard CT scans into PET-quality images, eliminating the need for radioactive tracers and scarce cyclotron-produced isotopes. The core model, invented in 2021 by co-founder and CMIO Regent Lee, learns the statistical relationships between paired CT and PET scans and then synthesizes PET-equivalent images that clinicians can use for cancer detection, staging, and monitoring. CEO Sean Walsh states that clinical pilots with Mass General Brigham and UCSF Health have demonstrated diagnostic accuracy statistically indistinguishable from conventional chemical PET scans. The company is currently focused on lung cancer use cases and plans to extend its platform to colorectal and lymphoma imaging, with broader ambitions across radiology and other scientific domains. Future milestones include an FDA-regulated clinical trial followed by commercial pilots to validate market adoption. Financially, RADiCAIT emerged from stealth with $1.7 million in pre-seed funding and has initiated a $5 million seed raise to support its regulatory and clinical programs. No revenue or user figures were disclosed in the article.

Total raised

$2M

Funding rounds

1

Latest round

Seed

Latest activity

Oct 2025