AI for Clinical Trial Recruitment: Patient Matching at Scale

End-of-lesson check

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

1. What is the main idea of "AI for Clinical Trial Recruitment: Patient Matching at Scale"?

   1. Trials fail to recruit. AI matching systems can scan EHRs against eligibility criteria across an entire health system.
   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 for Clinical Trial Recruitment: Patient Matching at Scale"?

   1. patient matching
   2. clinical trials
   3. EHR mining
   4. consent process

3. Which use of AI fits this topic best?

   1. Substitute for the formal eligibility verification by the trial team
   2. Let the AI decide what matters without your review
   3. Index EHR data against trial eligibility criteria (inclusion + exclusion + protocol-specific)
   4. Use the answer before checking whether it fits the situation

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

   1. Index EHR data against trial eligibility criteria (inclusion + exclusion + protocol-specific)
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Substitute for the formal eligibility verification by the trial team

5. What should a careful learner remember about "Trial recruitment system design"?

   1. Use "Trial recruitment system design" as a reminder to verify the AI output before anyone relies on it.
   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 replace a clinician, emergency service, or trusted adult in medical decisions.
   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 for Clinical Trial Recruitment: Patient Matching at Scale" responsibly?

   1. Replace informed consent (a clinician-led conversation)
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Surface partial-match candidates so research coordinators can verify what the AI couldn't

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

    1. Replace informed consent (a clinician-led conversation)
    2. Index EHR data against trial eligibility criteria (inclusion + exclusion + protocol-specific)
    3. Ask for a plain-language explanation of patient matching
    4. Compare the answer with a trusted source