Early detection is key to beating cancer.
KAIoptix uses predictive AI to triage biopsy samples, helping pathologists focus on the most urgent cases sooner — without changing existing lab workflows.
01 — Predict
Our AI rapidly pre-screens biopsy slides, identifying abnormal tissue regions associated with cancer risk.
02 — Prioritise
Suspicious samples are automatically triaged, ensuring high-risk cases are reviewed first by qualified pathologists.
03 — Accelerate
Earlier prioritisation enables faster diagnosis, earlier treatment decisions, and improved patient outcomes.
How Biopsies Are Reviewed Today & How KAIoptix Improves It
~1,200 biopsy samples are processed daily.
KAIoptix ensures the most suspicious cases are reviewed first — not by chance.
problem
For cancer patients, every day matters.
Pathology laboratories process hundreds to thousands of biopsy samples daily, yet only a small percentage are malignant. Despite this, samples are typically reviewed in the order they arrive — not by clinical urgency.
This results in avoidable delays for patients who most urgently need answers.
At the same time, pathology services worldwide face increasing pressure from rising demand, ageing populations, and a global shortage of qualified pathologists.
Training new pathologists takes over a decade. Demand is growing faster than capacity.
The current system is under strain.
solution
KAIoptix introduces predictive triage before review.
Our software emulates the way pathologists pre-screen slides, using AI to identify abnormal tissue regions and assign a predicted cancer-risk priority.
Instead of reviewing samples by arrival time, laboratories can focus on the most suspicious cases first — without disrupting existing workflows or replacing clinical judgement.
Predictive triage only. Final diagnosis remains with the pathologist.
our team
Built by experts in pathology, imaging, and artificial intelligence
Ken Salisbury
Founder, Sales and Technical support
An experienced entrepreneur with nearly 50 years of experience in light microscopy and image analysis expertise. Founded Improvision in 1990, a company developing software and hardware to visualise and measure living cells in 2D, 3D and timelapse 3D. Improvision was acquired by Perkin-Elmer in 2007.
David McCleary
ai and software developent
David is a seasoned AI software developer and R&D manager with over 14 years of experience. He played an instrumental role in the formation of PathXL, which was spun out of Queens in Belfast, and went on to be acquired by both Philips and Cirdan. David is widely recognized for his significant accomplishments in the field.
Prof David Harrison
Pathologist
David is our clincal lead scientist, based at St Andew’s, is a Professor of Pathology and Director of the ind Centre for AI in Res Diagnostics his wealth of knowledge is invaluable.
Prof Ivan Tyukin
AI lead
Alan Turin AI Fellow and Prof of Mathematics at KCL. Ivans team will spear head our technology drive.
Development Roadmap
KAIoptix uses predictive AI to triage biopsy samples, helping pathologists focus on the most urgent cases sooner — without changing existing lab workflows.
01 — idea
Our AI rapidly pre-screens biopsy slides, identifying abnormal tissue regions associated with cancer risk.
02 — proof of concept
Suspicious samples are automatically triaged, ensuring high-risk cases are reviewed first by qualified pathologists.
03 — MVP Prototype
Earlier prioritisation enables faster diagnosis, earlier treatment decisions, and improved patient outcomes.
04 — testing & Validation
Earlier prioritisation enables faster diagnosis, earlier treatment decisions, and improved patient outcomes.
05 — Accelerate
Earlier prioritisation enables faster diagnosis, earlier treatment decisions, and improved patient outcomes.
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