Summary: This article reviews AI approaches for research data curation with an academic and regulatory focus. It synthesizes automated data cleaning, anomaly detection, and nursing roles in ensuring high quality trial data capture.
High quality data underpins reliable clinical research. Nurses often collect and steward trial data at the bedside. AI can detect missingness, inconsistent entries, and protocol deviations in near real time to support corrective action.
Technically systems use rule based validation, anomaly detection on time series, NLP to reconcile free text entries, and audit trails for provenance. Validation includes concordance with manual monitoring, reduction in query rates, and regulatory acceptability for source data verification.
Guidance: For research teams: integrate AI quality checks into eCRFs, train nursing staff on data stewardship practices, pilot near real time monitoring with human review, document audit trails for regulators, and measure reductions in data queries and protocol deviations.
Conclusion: AI data quality tools can reduce monitoring burden and improve trial integrity when combined with nursing stewardship, human review, and regulatory documentation.
Final Summary: automated validation; anomaly detection; NLP reconciliation; audit trails; nursing training
Useful Facts: clinical trials | data management | nursing research
Related Topics: nursing;clinical research AI detects data anomalies earlier than periodic monitoring; Near real time checks reduce query volume; Nursing stewardship improves data fidelity; Audit trails support regulatory review; Human review remains essential for adjudication