Summary: This article reviews AI applications that streamline clinical trial recruitment with an academic and practical focus. It synthesizes eligibility screening from EHRs, NLP of pathology reports, and nursing workflows for consent and enrollment coordination.

Low enrollment slows oncology research. Nurses facilitate screening, consent, and retention. AI can pre screen EHRs to flag potentially eligible patients, extract staging and biomarker data, and present prioritized lists to research nurses.

Technically systems use rule based filters augmented with NLP to extract tumor markers, staging and prior therapies. Ranking algorithms score match likelihood and integrate scheduling constraints. Validation requires concordance with manual screening, prospective enrollment impact metrics, and attention to privacy and equitable outreach.

Guidance: For research teams: integrate AI screening into tumor boards and nursing workflows, require human confirmation before outreach, co design patient communication scripts with nurses, pilot in single disease cohorts, and monitor enrollment diversity and consent rates.

Conclusion: AI assisted recruitment can increase trial accrual and reduce screening burden when integrated with nursing coordination, human oversight, and equity monitoring.

Final Summary: EHR screening + NLP extraction + nurse coordination. Priorities include human confirmation, privacy safeguards, and enrollment diversity monitoring.

Useful Facts: clinical research | oncology | nursing coordination

Related Topics: nursing;clinical research NLP extracts biomarker data; Ranking reduces screening time; Nurse confirmation preserves consent quality; Pilot studies measure accrual improvement; Equity audits ensure representative enrollment