Clinical AIACADEMY
The learning path

MODULE 4 / 8

Trustworthy AI: Ethics, Risk, and Regulation in Clinical Practice

Jump to

Section 1 of 9

In this module

Topics to explore:

  • Describe the WHO’s six ethical principles for AI in health
  • Identify common sources of bias in training data and model behaviour
  • Distinguish between transparent and post hoc explainable models
  • Understand the clinician’s legal and ethical responsibilities when using AI tools
  • Explain why ongoing oversight is critical after deployment

Slide 1: Why Ethics and Governance Matter

AI in healthcare isn’t just about accuracy. It’s about safety, fairness, transparency, and accountability. An algorithm might work brilliantly in a lab—but if it amplifies bias, fails in new populations, or can’t be explained, it becomes a liability.

The WHO (2021) and FDA (2021) both stress that governance is central to trustworthy AI. Let’s unpack why that matters for clinicians.