Harrison.ai builds AI systems that sit inside the clinical pipeline - radiology and pathology - and the security implications are immediate: these are tools processing millions of de-identified medical images across 1,000+ deployment sites on four continents, cleared for use in over 40 countries. The threat model is not abstract. It encompasses protected health information traversing imaging networks, model integrity across distributed inference points, and the regulatory surface area that comes with operating diagnostic support software at this scale. Founded in 2018 and backed by over US$240 million in funding, the company has analyzed more than 6.7 million medical imaging cases to date.
The product stack splits across radiology AI and pathology AI. Radiology solutions surface urgent findings and reduce worklist backlog; pathology AI extends diagnostic support into histopathology workflows. Both are clinician-led in design - the company was founded by practicing clinicians - which means requirements are shaped by real diagnostic pressure rather than hypothetical use cases. Over 3,500 clinicians currently use these tools in production.
For security and infrastructure roles, the operational picture is one of a regulated, globally distributed AI platform handling sensitive healthcare data at scale. Compliance regimes span dozens of jurisdictions. The engineering challenge involves securing data pipelines, protecting model supply chains, and ensuring that inference endpoints across a thousand-plus sites maintain integrity without degrading clinical latency. This is healthcare AI with real deployment gravity.
