Is The Industry Ready To Trust LLMs With Chart Review?

Large language models are transforming clinical chart review from a manual bottleneck into a scalable data infrastructure capable of generating regulatory-grade evidence at machine speed. This piece explores how modern LLM pipelines can extract structured, CDISC-compliant data from complex medical records with human-level accuracy—provided they are deployed within validated frameworks built for traceability, oversight, and deterministic output. It highlights why successful AI adoption in clinical research depends less on model selection and more on infrastructure design, including multi-model consensus, strict extraction guardrails, and mandatory human review. Here, we examine how Castor Catalyst combines secure ingestion, intelligent CRF mapping, confidence scoring, and native EDC integration to accelerate evidence generation while maintaining compliance and data integrity across real-world research workflows.