Evaluating local open-source large language models for data extraction from unstructured reports on mechanical thrombectomy in patients with ischemic stroke.

This study highlights the potential of using LLMs for automated clinical data extraction from medical reports. Incorporating HITL annotations enhances precision and also ensures the reliability of the extracted data. This methodology presents a scalable privacy-preserving option that can significantly support clinical documentation and research endeavors.