LLM-Assisted Tool Streamlines SNOMED CT Mapping

Researchers at Kakao Healthcare and collaborators published in JMIR Medical Informatics 2026 describe an LLM-assisted tool using GPT-4o to automate SNOMED CT mapping and new-concept authoring across nine South Korean university hospitals. The system achieved top-5 diagnostic mapping accuracies ranging from 89.7% to 98.7%, cut manual mapping rates by 30% and overall workload by up to 90%. Time to map and create concepts fell ~75%, with duplicates down 83% and rule violations down 72%.