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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 support. To comply with Google's AI Principles and regulatory requirements, which TWO actions are essential? (Select 2)
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 for all diagnostic suggestions
Option A is correct because Google's AI Principles require that high-stakes applications such as medical diagnosis keep humans in the loop, ensuring a qualified clinician reviews and approves every AI-generated diagnostic suggestion before it affects patient care. Option C is correct because processing patient data for diagnosis support triggers legal obligations under GDPR and comparable privacy regulations, including lawful basis, data minimization, and protection of special-category health data. Option B is not essential here: a Model Card is useful documentation for transparency, but it is not a mandatory action for regulatory compliance in this scenario. Option D is not essential: SynthID watermarks AI-generated content to aid provenance, which is irrelevant to diagnostic decision support. Option E is not essential: using a larger model may improve accuracy but does not by itself satisfy AI Principles or regulatory requirements.
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 for all diagnostic suggestions
Why this is correct
Human-in-the-loop review keeps a qualified clinician accountable for every diagnostic suggestion, satisfying Google's AI Principles requirement that high-stakes medical AI remains subject to human oversight and cannot autonomously determine care. This directly addresses the regulatory constraint that generative outputs supporting diagnosis must be verified by a responsible professional before affecting patient treatment.
- ✗
Publish a Model Card for the model
Why it's wrong here
A Model Card documents intended use, limitations and evaluation data; it does not itself satisfy regulatory duties such as clinical validation, audit trails or human oversight for diagnosis. It is tempting because transparency documentation is a real Google AI Principles practice, and would be correct when the requirement is communicating model characteristics to downstream users.
- ✓
Ensure the system complies with GDPR and other privacy regulations for patient data
Why this is correct
GDPR compliance is legally mandatory for processing patient health data, which qualifies as special-category data under Article 9, requiring an explicit lawful basis and heightened safeguards. This satisfies the stem's regulatory requirements constraint, since medical diagnosis support necessarily handles sensitive personal data that Google's AI Principles also demand be protected.
- ✗
Use SynthID to watermark all output
Why it's wrong here
SynthID watermarks outputs as AI-generated for provenance; it neither validates diagnostic accuracy nor meets medical device regulation, so it cannot satisfy the compliance actions required here. It is tempting because it is a genuine Google DeepMind provenance technology, and would be correct when the requirement is disclosing synthetic content rather than assuring clinical safety.
- ✗
Use a larger model to improve accuracy
Why it's wrong here
Scaling model size may raise benchmark accuracy but delivers no validation, oversight or documentation, so it cannot satisfy Google's AI Principles or medical regulation. It is tempting because larger models often perform better on tasks, and would be correct when the requirement is improving raw predictive quality rather than demonstrating compliance.
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