RADiCAIT was founded with a singular conviction: that functional imaging should not be a privilege reserved for patients at well-resourced centres. We're building the technology to make it universal.
PET imaging is one of the most powerful diagnostic tools in modern medicine. It reveals how tissue is functioning — not just what it looks like — making it invaluable for cancer staging, neurological diagnosis, and cardiac assessment. Yet fewer than 5% of patients who need a PET scan ever receive one.
The barriers are structural: radioactive tracers expire within hours, PET scanners cost tens of millions of pounds, and skilled nuclear medicine specialists are scarce. RADiCAIT exists to make these barriers irrelevant.
Explore Our TechnologyInsilico PET® closes the diagnostic gap between CT and PET imaging through AI.
RADiCAIT brings together deep expertise in radiology, machine learning, clinical medicine, and operations — all focused on a single transformative goal.
Radiology AI researcher and entrepreneur. Led foundational research in CT-to-PET image translation using generative neural networks.
Oxford professor and UKRI Innovation Fellow. Bridges clinical imaging expertise with AI research to ensure Insilico PET® meets the highest diagnostic standards.
Computational physicist and three-time technology founder. Leads the ML architecture, inference optimisation, and clinical integration platform.
Specialist in scaling deep-tech startup operations. Drives strategic partnerships, clinical deployment programmes, and regulatory strategy.
Every claim we make is grounded in robust validation against clinical ground truth. We publish our research and welcome independent scrutiny.
Our technology exists to improve patient outcomes. Every design decision is evaluated against the question: does this help clinicians help patients better?
Functional imaging should not be restricted to wealthy health systems. We build with scalability and affordability as core design constraints, not afterthoughts.
Clinicians must be able to trust and interrogate AI-derived findings. We prioritise explainability and appropriate uncertainty quantification in every model output.
We work within regulatory frameworks, engage with ethics bodies, and partner with clinical institutions to ensure safe, validated deployment of our technology.
We are building infrastructure for the next decade of medical imaging — not optimising for short-term metrics. The clinical problems we're solving require durable solutions.
From academic breakthrough to clinical deployment — a focused, deliberate path toward transforming global radiology practice.
Core research into AI-driven CT-to-PET image synthesis begins at a leading UK research institution. First generative model achieves statistically significant metabolic concordance.
Pilot study across oncology cohort demonstrates 91% concordance with ground-truth PET imaging. Results submitted for peer review.
RADiCAIT is incorporated in London, bringing together the founding team of clinicians, physicists, and operators to commercialise the technology.
Insilico PET® extended to neurology and cardiology cohorts. Model achieves 94% diagnostic concordance across all three clinical areas.
Deployment partnerships established with NHS Trusts and academic medical centres. DICOM-native API platform enters active clinical research use.
Whether you're a clinician, researcher, health system, or pharma partner — we'd love to explore how Insilico PET® can serve your needs.