Clinical Trial Patient Matching: AI-Assisted Eligibility Screening

Clinical trials enroll only 3-5% of eligible patients, partly because eligibility screening is time-intensive. AI can assist in matching patients to trials by comparing patient profiles to eligibility criteria — expanding research participation and patient access to cutting-edge treatments.

10 min · Reviewed 2026

The enrollment gap in clinical research

The US runs over 400,000 registered clinical trials, but fewer than 5% of eligible patients enroll. Screening patients against complex inclusion/exclusion criteria is labor-intensive, and many oncologists and specialists simply don't have time to review the full trial landscape for each patient. AI can match de-identified patient profiles to trial eligibility criteria in seconds — surfacing options that might never have been identified manually.

Trial matching prompt

De-identify all inputs before using any non-BAA-covered AI tool
AI matching is a first-pass screen — a clinical research coordinator or PI must confirm eligibility
Exclusion criteria are as important as inclusion — a single exclusion can disqualify a patient
Phase matters: Phase 1 trials prioritize safety; Phase 3 trials most closely approximate standard care
Underrepresented populations (minorities, elderly, pediatric) are systematically under-enrolled — AI matching can help address this by proactively surfacing trials with equity-focused eligibility criteria

Equity in clinical trial enrollment

Historical clinical trial populations have underrepresented women, racial and ethnic minorities, and elderly patients — producing drugs and treatments whose efficacy and safety profiles are less well-characterized in these groups. AI-assisted matching programs that actively flag under-enrollment by demographic subgroup and route eligible patients to trials seeking to address this gap are a tool for both research integrity and health equity.

The big idea: AI screens thousands of trials against a patient profile in seconds. The clinician and research team confirm eligibility and obtain informed consent.

End-of-lesson check

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

What is the core idea behind "Clinical Trial Patient Matching: AI-Assisted Eligibility Screening"?
1. Clinical trials enroll only 3-5% of eligible patients, partly because eligibility screening is time-intensive. AI can assist in matching patients to trials by comparing patient profiles to eligibility criteria — expanding research participation and patient access to cutting-edge treatments.
2. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
3. Long stretches of stillness
4. Apply AI in Healthcare Revenue Cycle in a live project this week
Which term best describes a foundational idea in "Clinical Trial Patient Matching: AI-Assisted Eligibility Screening"?
1. inclusion criteria
2. clinical trial matching
3. exclusion criteria
4. ClinicalTrials.gov
A learner studying Clinical Trial Patient Matching: AI-Assisted Eligibility Screening would need to understand which concept?
1. clinical trial matching
2. exclusion criteria
3. inclusion criteria
4. ClinicalTrials.gov
Which of these is directly relevant to Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. clinical trial matching
2. inclusion criteria
3. ClinicalTrials.gov
4. exclusion criteria
Which of the following is a key point about Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. De-identify all inputs before using any non-BAA-covered AI tool
2. AI matching is a first-pass screen — a clinical research coordinator or PI must confirm eligibility
3. Exclusion criteria are as important as inclusion — a single exclusion can disqualify a patient
4. Phase matters: Phase 1 trials prioritize safety; Phase 3 trials most closely approximate standard ca…
Which of these does NOT belong in a discussion of Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. AI matching is a first-pass screen — a clinical research coordinator or PI must confirm eligibility
2. Exclusion criteria are as important as inclusion — a single exclusion can disqualify a patient
3. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
4. De-identify all inputs before using any non-BAA-covered AI tool
What is the key insight about "Trial matching prompt" in the context of Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Long stretches of stillness
3. Given the following de-identified patient profile, identify clinical trials on ClinicalTrials.
4. Apply AI in Healthcare Revenue Cycle in a live project this week
What is the key insight about "AI matching is not informed consent" in the context of Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Long stretches of stillness
3. Apply AI in Healthcare Revenue Cycle in a live project this week
4. Identifying a patient as potentially eligible for a clinical trial is the start of a process, not the end.
What is the key insight about "Human review boundary" in the context of Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Use AI to organize questions, summaries, and options, not to diagnose or choose treatment.
2. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
3. Long stretches of stillness
4. Apply AI in Healthcare Revenue Cycle in a live project this week
Which statement accurately describes an aspect of Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. The US runs over 400,000 registered clinical trials, but fewer than 5% of eligible patients enroll.
3. Long stretches of stillness
4. Apply AI in Healthcare Revenue Cycle in a live project this week
What does working with Clinical Trial Patient Matching: AI-Assisted Eligibility Screening typically involve?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Long stretches of stillness
3. Historical clinical trial populations have underrepresented women, racial and ethnic minorities, and elderly patients — producing drugs and …
4. Apply AI in Healthcare Revenue Cycle in a live project this week
Which of the following is true about Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Long stretches of stillness
3. Apply AI in Healthcare Revenue Cycle in a live project this week
4. The big idea: AI screens thousands of trials against a patient profile in seconds.
Which best describes the scope of "Clinical Trial Patient Matching: AI-Assisted Eligibility Screening"?
1. It focuses on Clinical trials enroll only 3-5% of eligible patients, partly because eligibility screening is time-
2. It is unrelated to healthcare workflows
3. It applies only to the opposite beginner tier
4. It was deprecated in 2024 and no longer relevant
Which section heading best belongs in a lesson about Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Trial matching prompt
3. Long stretches of stillness
4. Apply AI in Healthcare Revenue Cycle in a live project this week
Which section heading best belongs in a lesson about Clinical Trial Patient Matching: AI-Assisted Eligibility Screening?
1. Find out more about AI Helps Match People Who Need New Organs by asking an AI a …
2. Long stretches of stillness
3. Equity in clinical trial enrollment
4. Apply AI in Healthcare Revenue Cycle in a live project this week

← Back to interactive lesson

Tendril · Adults & Professionals · AI in Healthcare