AI and a clinical trial eligibility skim

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-creators-healthcare-AI-and-clinical-trial-eligibility-skim-r10a3-adults

1. What is the core idea behind "AI and a clinical trial eligibility skim"?

   1. Use AI to compare a patient summary against trial inclusion and exclusion criteria, then surface a likely-fit list.
   2. Brushing still beats AI every time
   3. diabetes
   4. ethics

2. Which term best describes a foundational idea in "AI and a clinical trial eligibility skim"?

   1. exclusion criteria
   2. inclusion criteria
   3. eligibility
   4. screening

3. A learner studying AI and a clinical trial eligibility skim would need to understand which concept?

   1. inclusion criteria
   2. eligibility
   3. exclusion criteria
   4. screening

4. Which of these is directly relevant to AI and a clinical trial eligibility skim?

   1. inclusion criteria
   2. exclusion criteria
   3. screening
   4. eligibility

5. Which of the following is a key point about AI and a clinical trial eligibility skim?

   1. Map patient facts to numbered inclusion/exclusion items.
   2. Flag missing data needed to make a decision.
   3. Rank trials by how many criteria match.
   4. Brushing still beats AI every time

6. What is one important takeaway from studying AI and a clinical trial eligibility skim?

   1. Pull the patient's chart for missing data.
   2. Confirm the patient is actually a fit.
   3. Replace the IRB-approved screening process.
   4. Brushing still beats AI every time

7. What is the key insight about "Prompt: eligibility table" in the context of AI and a clinical trial eligibility skim?

   1. Brushing still beats AI every time
   2. diabetes
   3. Paste: 'Patient summary: <text>. Trial criteria (numbered): <list>.
   4. ethics

8. What is the key insight about "Watch out: 'unknown' as 'no'" in the context of AI and a clinical trial eligibility skim?

   1. Brushing still beats AI every time
   2. diabetes
   3. ethics
   4. AI may treat missing data as a fail. Make 'unknown' a real category and route those rows to a human to chase down.

9. Which statement accurately describes an aspect of AI and a clinical trial eligibility skim?

   1. Matching patients to trials is a structured comparison task. AI can do the first pass; the research coordinator does the second.
   2. Brushing still beats AI every time
   3. diabetes
   4. ethics

10. Which best describes the scope of "AI and a clinical trial eligibility skim"?

    1. It is unrelated to healthcare workflows
    2. It focuses on Use AI to compare a patient summary against trial inclusion and exclusion criteria, then surface a l
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about AI and a clinical trial eligibility skim?

    1. Brushing still beats AI every time
    2. diabetes
    3. What AI does well here
    4. ethics

12. Which section heading best belongs in a lesson about AI and a clinical trial eligibility skim?

    1. Brushing still beats AI every time
    2. diabetes
    3. ethics
    4. What AI cannot do

13. Which of the following is a concept covered in AI and a clinical trial eligibility skim?

    1. inclusion criteria
    2. exclusion criteria
    3. eligibility
    4. screening

14. Which of the following is a concept covered in AI and a clinical trial eligibility skim?

    1. inclusion criteria
    2. exclusion criteria
    3. eligibility
    4. screening

15. Which of the following is a concept covered in AI and a clinical trial eligibility skim?

    1. inclusion criteria
    2. exclusion criteria
    3. eligibility
    4. screening