Contribution · Application — Healthcare

AI for Clinical Trial Patient Matching

Clinical trials chronically under-enroll — 80% miss recruitment targets, delaying therapies. LLMs can parse dense eligibility criteria against structured and unstructured EMR data to flag candidates for human screening. The win is efficiency; the risk is privacy, bias in who gets matched, and inadvertently steering vulnerable patients. Build with clinician oversight, consent-first workflows, and rigorous eval against trial coordinators.

Application facts

Domain
Healthcare
Subdomain
Clinical research
Example stack
Claude Sonnet 4.7 or GPT-5 for criteria parsing · LangGraph agent for iterative patient screening · LlamaIndex over HL7 FHIR EMR extracts · pgvector for semantic patient-trial similarity · Audit log + clinician review UI · ClinicalTrials.gov / CTRI feed integration

Data & infrastructure needs

  • Structured EMR data (FHIR) with labs, meds, diagnoses
  • Trial protocols and eligibility criteria
  • Consent and preference records
  • Demographic audit data for bias monitoring

Risks & considerations

  • Privacy — re-identification from combining EMR and genomic signals
  • Bias — under-matching women, elderly, and minority populations
  • Regulatory — DPDPA, HIPAA, GDPR, ICMR guidelines for research
  • Coercion risk — aggressive matching of vulnerable patients
  • Hallucination in interpreting complex inclusion/exclusion logic

Frequently asked questions

Is AI for clinical trial matching safe?

Only as a decision-support layer: the LLM produces ranked candidates, a clinical research coordinator confirms, and the patient provides informed consent. Never deploy auto-enrollment or one-click outreach. Ground matches in FHIR-structured data with citations back to the chart.

What LLM is best for clinical trial matching?

Claude Opus 4.7 and GPT-5 both handle the complex boolean logic in eligibility criteria. Pair with a deterministic eligibility engine — let the LLM extract structured criteria, then evaluate against structured data rather than free-text reasoning.

Regulatory concerns for clinical trial matching?

India DPDPA + ICMR National Ethical Guidelines, US HIPAA + Common Rule, EU GDPR + CTR. Require IRB/EC approval for the matching workflow, not just the trial. Document the model, eval results, and bias monitoring.

Sources

  1. ICMR National Ethical Guidelines for Biomedical Research — accessed 2026-04-20
  2. ClinicalTrials.gov — NLM — accessed 2026-04-20
  3. CTRI — Clinical Trials Registry India — accessed 2026-04-20

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