Tendril · Adults & Professionals · AI in Healthcare
AI and a clinical trial eligibility skim
Use AI to compare a patient summary against trial inclusion and exclusion criteria, then surface a likely-fit list.
9 min · Reviewed 2026
The premise
Matching patients to trials is a structured comparison task. AI can do the first pass; the research coordinator does the second.
What AI does well here
Map patient facts to numbered inclusion/exclusion items.
Flag missing data needed to make a decision.
Rank trials by how many criteria match.
What AI cannot do
Confirm the patient is actually a fit.
Pull the patient's chart for missing data.
Replace the IRB-approved screening process.
Practice this safely
Use a real but low-risk workflow from your day. Treat AI as a drafting and organizing layer, then verify the output before anyone relies on it.
Ask AI to explain inclusion criteria in plain language, then underline anything that sounds uncertain or too broad.
Give it one detail from "AI and a clinical trial eligibility skim" and ask for two possible next steps plus one reason each step might be wrong.
Check exclusion criteria against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson check
10 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
What is the main idea of "AI and a clinical trial eligibility skim"?
Use AI to compare a patient summary against trial inclusion and exclusion criteria, then surface a likely-fit list.
Use AI as the final authority for the whole decision
Avoid checking the answer once it sounds polished
Focus only on speed instead of judgment
Which concept is most central to "AI and a clinical trial eligibility skim"?
exclusion criteria
inclusion criteria
eligibility
screening
Which use of AI fits this topic best?
Confirm the patient is actually a fit.
Let the AI decide what matters without your review
Map patient facts to numbered inclusion/exclusion items.
Use the answer before checking whether it fits the situation
Which limitation should you watch for in this topic?
Map patient facts to numbered inclusion/exclusion items.
Explain the topic in plain language
Organize a draft for human review
Confirm the patient is actually a fit.
What should a careful learner remember about "Prompt: eligibility table"?
Use AI to organize questions, then involve a qualified adult or clinician before acting.
Skip the context so the tool can guess faster
Treat the output as private even after sharing it online
Use the answer without checking the source
You want to use AI after this lesson. What is the safest next step?
Act immediately because the AI answer is written clearly
AI cannot replace a clinician, emergency service, or trusted adult in medical decisions.
Hide uncertainty so the final answer looks cleaner
Use private or sensitive details before checking permission
How should AI output about inclusion criteria be treated?
As proof that no other source is needed
As a replacement for context, consent, or expert review
As a draft or helper output that still needs human judgment and verification
As something that becomes correct when it sounds confident
Name one way to verify an AI answer about inclusion criteria.
Which action would help you apply "AI and a clinical trial eligibility skim" responsibly?
Pull the patient's chart for missing data.
Use the tool to avoid thinking through the tradeoff
Keep going even if the output conflicts with a trusted source
Flag missing data needed to make a decision.
Which choice is a bad use of AI for this lesson?
Pull the patient's chart for missing data.
Map patient facts to numbered inclusion/exclusion items.
Ask for a plain-language explanation of exclusion criteria
