Clinical Intelligence

How clinical intelligence is created, strengthened, and learned through collaboration

Clinical intelligence underpins how cancer care decisions are made in practice. Because these decisions span multiple domains of expertise and evolve over time, clinical intelligence must be created, tested, and learned from collaboratively—within MDTs and across health systems.

Our approach to clinical intelligence is grounded in three fundamentals.

First, multidisciplinary teams are the unit of clinical intelligence.

In cancer care, patient-specific decisions are formed within clinical MDTs through the interaction of clinical expertise across surgery, oncology, radiology, pathology, nursing, and allied specialities.

Multidisciplinary collaboration is the mechanism through which intelligence is developed, tested, and improved over time. We use the MDT concept as an organising anchor for collaboration around clinical decision-making, enabling insights from data scientists, AI specialists, biologists, and other experts to contribute to the advancement of clinical intelligence—without altering or expanding MDT practice.

In this way, broader collaboration strengthens the quality, learning, and evolution of clinical intelligence, while the clinical MDT remains the locus of responsibility for individual patient decisions.

Clinical AI

Clinical intelligence supported by technology unlocks real-time learning across various settings.

We engineer clinical AI systems specifically for cancer care. These systems work with real-world clinical information, such as radiology, pathology, laboratory data, and structured clinical feedback.

Our Clinical AI does not replace human judgment or automate decisions. Instead, it underpins collaboration by making patterns visible, surfacing relevant context, and supporting shared reasoning across cases and institutions.