Clinical AIACADEMY
The learning path

MODULE 3 / 8

How AI Performs in Clinical Practice: Real Cases, Real Limits

Jump to

Section 1 of 10

In this module

Topics to explore:

  • Critically appraise clinical AI studies with respect to data quality, labels, and validation
  • Explain why external validation is essential for trustworthy AI
  • Interpret common metrics (e.g., AUROC, Dice score) and their limitations
  • Recognise the difference between internal accuracy and real-world utility
  • Assess when and how AI tools should support—not replace—clinical decision-making

Slide 1: From Theory to Practice

You now understand that AI learns from examples, not logic. But how does this play out in real clinical settings? In this module, we’ll walk through key clinical studies that claimed “expert-level” AI performance. Some delivered. Some didn’t.

As always, the devil is in the details: how the data was collected, labelled, and tested.