Clinical Trials & CROs
Accelerating trial design, recruitment, and data extraction with AI
Industry Challenge
Clinical trials generate millions of data points — protocol documents, eligibility criteria, adverse event narratives, patient-reported outcomes, and case report forms — that AI can help process and analyse at speed. Clinical trial AI needs training data annotated by professionals who understand trial methodology, regulatory requirements, and medical terminology.
How SCILabel Serves This Industry
Data Collection
We source clinical trial protocol documents, CRF data, adverse event narratives, and trial result datasets from CRO and academic research partners under appropriate data sharing frameworks.
Data Annotation & Labeling
Our clinical researchers and Trial Coordinators annotate trial eligibility criteria (inclusion/exclusion criterion classification and entity extraction), adverse event MedDRA term coding, SAE narratives with causality labels, and protocol deviation classifications.
Data & Model Evaluation
Expert evaluators assess trial data extraction AI accuracy against manual CRF extraction ground truth, benchmark eligibility criteria NLP against clinical trial registries, and evaluate adverse event coding accuracy against MedDRA expert coders.
Annotation Types & Formats
- Eligibility criterion classification: inclusion/exclusion, entity type
- Adverse event MedDRA term normalisation and causality labeling
- SAE narrative structured data extractionI
- Protocol deviation classification
- Trial result document section classification
Specialist Workforce Tracks
Track 1 (Medical NLP) and Track 4 (Genomics & Biomedical): Clinical Researchers, Trial Coordinators, Pharmacists, Biomedical Scientists.