AI Fundamentals for Imaging and Oncology
A complete, interactive Articulate Rise 360 module that helps radiologists and oncologists understand and critically appraise AI in cancer care. This is the real thing: play it below.
The problem
AI is already in the imaging and cancer pathway, which means it already touches patient care. But most clinical AI training treats it as a novelty, or drowns busy clinicians in technical detail they do not need. Radiologists and oncologists do not need to become data scientists. They need to know enough to challenge an AI output, and to know when a regulatory mark does not mean a tool is safe for their patients.
The learner
Expert, time-poor clinicians, exactly the audience I design for. They are sceptical of anything that wastes their time, and rightly so. The module had to respect that: plain language, no hype, and every idea anchored to the one thing the College anchors all its work to, patients' best interests.
Outcomes first. The module is built backwards from three clear objectives: explain how these AI tools produce their outputs and why training data shapes them; distinguish regulatory approval from clinical validation; and critically appraise an AI output, recognising the failure modes that matter clinically.
The design approach
- Five focused lessons, each making one point that matters: What the tools actually do, Approval is not proof, What would you do?, Clearance is not sign-off, and Your question to take away.
- Decision-based practice. The What would you do? lesson puts clinicians in a realistic situation and asks them to judge an AI output, so the learning is rehearsal, not recall.
- Plain-language explanation of how the tools work and why the data they were trained on shapes what they give you, without turning it into a computer-science lecture.
- A takeaway question the clinician can carry back to the ward, turning the module into a change in behaviour rather than a completed tick box.
- Built accessibly in Rise 360, responsive across devices, so it works in the gaps of a real clinical day.
Why it matters
This is the difference between AI training that reassures a governance committee and AI training that actually changes how a clinician reads an AI result. It teaches judgement, not trivia, and it treats the learner as the expert they are.
Further reading: this site's accessibility statement, and how AI supports clinical decisions, not replaces them.