hardMultiple Choice
Generative AI Leader Practice Question: A healthcare organization deploys a generative AI…
A healthcare organization deploys a generative AI model to assist in diagnosing rare diseases from medical images. To comply with the EU AI Act's requirements for high-risk AI systems, what is the MOST critical control they must implement?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Implement a human-in-the-loop review process with the ability to override AI recommendations
The EU AI Act requires human oversight for high-risk AI systems, including the ability for humans to override or stop the system's decisions.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Implement a human-in-the-loop review process with the ability to override AI recommendations
Why this is correct
Human-in-the-loop review with override capability satisfies the EU AI Act's high-risk requirement for effective human oversight. For rare-disease diagnosis, clinicians must be able to scrutinise and reject AI recommendations, ensuring meaningful control over decisions affecting patient safety and fundamental rights.
- ✗
Use a model with a high F1 score to minimize the need for human review
Why it's wrong here
A high F1 score does not remove the EU AI Act's Article 14 requirement for effective human oversight of high-risk systems; accuracy metrics cannot substitute for oversight. It is tempting because F1 balances precision and recall, and would suit model selection when comparing classifiers on imbalanced diagnostic data.
- ✗
Ensure the model is trained on synthetic data only to avoid privacy concerns
Why it's wrong here
Synthetic-only training does not satisfy the EU AI Act's data governance duties, which require relevant, representative, sufficiently accurate and complete datasets; synthetic data can embed bias and miss rare-disease presentations. It is tempting because synthetic data reduces re-identification risk, and would suit privacy-preserving development when real data is unavailable.
- ✗
Deploy the model in a sandbox environment without real patient data
Why it's wrong here
A sandbox without real patient data cannot meet the EU AI Act's requirements for high-risk systems placed on the market, including conformity assessment, registration and post-market monitoring in real clinical use. It is tempting because sandboxes safely test models, and would suit pre-deployment validation or regulatory experimentation.
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