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Generative AI Leader Practice Question: Deploying a generative AI system for medical…
A company is deploying a generative AI system for medical diagnosis. Which TWO measures are essential for responsible AI in this high-stakes domain?
⚠ Common exam trap
Generative AI Leader often tests the balance between innovation and safety—candidates may select 'autonomous decisions' or 'publish patient data' as transparency measures, confusing openness with responsible AI.
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
✓
Ensure a human medical professional reviews all AI-generated diagnoses
Option B is correct because in a high-stakes medical diagnosis scenario, keeping a qualified human medical professional in the loop for every AI-generated diagnosis ensures accountability and mitigates the risk of harmful errors, which is a core principle of responsible AI (human oversight). Option D is correct because Model Cards provide structured documentation of a model's intended use, performance metrics, and known limitations, enabling clinicians to understand when and how much to trust the system's outputs. Together, these measures support transparency and human accountability, which are essential in clinical decision-making. Option A is not appropriate because autonomous AI decisions in emergencies remove the necessary human oversight and could cause serious harm. Option C is wrong because publishing all patient data would violate privacy and confidentiality regulations such as HIPAA, and transparency does not require exposing raw patient data. Option E is incorrect because restricting AI to administrative tasks does not address responsible AI for diagnosis and is not an essential measure for the described use case.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Allow the AI to make autonomous decisions in time-sensitive emergencies
Why it's wrong here
Autonomous AI decisions in emergencies remove the clinician oversight that medical diagnosis demands, so accountability for harmful errors cannot be assigned. It is tempting because faster triage genuinely saves lives, but autonomous emergency decisioning belongs in low-stakes, reversible contexts, not diagnosis.
- ✓
Ensure a human medical professional reviews all AI-generated diagnoses
Why this is correct
Human-in-the-loop review directly addresses the clinical risk constraint: an erroneous AI diagnosis could cause patient harm. Because generative models can hallucinate confidently, a qualified clinician must validate every output before it informs care, preserving professional accountability and satisfying medical-device oversight expectations for high-stakes decisions.
- ✗
Publish all patient data used for training to ensure transparency
Why it's wrong here
Publishing patient data breaches confidentiality and privacy law, and transparency about model behaviour does not require exposing identifiable records. It is tempting because transparency is a genuine responsible-AI principle, but it is satisfied through model cards, documentation and audit processes, not raw training data release.
- ✓
Provide model documentation (Model Cards) to clinicians detailing the system's limitations
Why this is correct
Model Cards expose a model's intended use, training data provenance, performance disparities and known failure modes, so clinicians can judge when outputs are untrustworthy. In medical diagnosis, where erroneous recommendations cause direct patient harm, this transparency satisfies the responsible-AI requirement for accountability and informed human oversight before acting on generated advice.
- ✗
Use the AI only for administrative tasks, not diagnosis
Why it's wrong here
Restricting the AI to administrative tasks abandons the diagnostic use case entirely, so it delivers no responsible-AI measure for the system being deployed. It is tempting because avoiding diagnosis sidesteps clinical risk, but the question asks how to govern diagnostic AI, not how to avoid it.
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Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Google Cloud exam blueprint
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