Lesson 1817 of 2244
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.
Adults & Professionals · AI in Healthcare · ~5 min read
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.
Key terms in this lesson
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.
- 1Ask AI to explain inclusion criteria in plain language, then underline anything that sounds uncertain or too broad.
- 2Give 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.
- 3Check exclusion criteria against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
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