AI and Clinical Trial Recruitment: Equitable Outreach Targeting

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-ethics-safety-AI-and-clinical-trial-recruitment-adults

1. What is the main idea of "AI and Clinical Trial Recruitment: Equitable Outreach Targeting"?

   1. AI-driven recruitment for clinical trials and equity in subject pools requires concrete process design — this lesson maps.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "AI and Clinical Trial Recruitment: Equitable Outreach Targeting"?

   1. recruitment
   2. clinical trials
   3. equity
   4. IRB review

3. Which use of AI fits this topic best?

   1. Substitute for counsel on jurisdiction-specific obligations.
   2. Let the AI decide what matters without your review
   3. Draft policy memos covering clinical trials obligations.
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Draft policy memos covering clinical trials obligations.
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Substitute for counsel on jurisdiction-specific obligations.

5. What should a careful learner remember about "AI and Clinical Trial Recruitment memo prompt"?

   1. Use AI to draft or organize ideas about clinical trials, then verify before acting.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. AI cannot make the human values or safety decision for you.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about clinical trials be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about clinical trials.

9. Which action would help you apply "AI and Clinical Trial Recruitment: Equitable Outreach Targeting" responsibly?

   1. Resolve the underlying value tradeoffs between competing stakeholders.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Generate vendor diligence checklists referencing recruitment.

10. Which choice is a bad use of AI for this lesson?

    1. Resolve the underlying value tradeoffs between competing stakeholders.
    2. Draft policy memos covering clinical trials obligations.
    3. Ask for a plain-language explanation of recruitment
    4. Compare the answer with a trusted source