A Multi-Stage Validation Framework for Trustworthy Large-scale Clinical Information Extraction using Large Language Models

arXiv:2604.06028v1 Announce Type: cross Abstract: Large language models (LLMs) show promise for extracting clinically meaningful information from unstructured health records, yet their translation into real-world settings is constrained by the lack of scalable and trustworthy validation approaches. Conventional evaluation methods rely heavily on annotation-intensive reference standards or incomplete structured data, limiting feasibility at population scale. We propose a multi-stage validation fr