aceDevelopment and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text
Systematized Nomenclature of Medicine-Clinical Terminology (SNOMED CT) is the principal international standard for semantic interoperability of clinical information, but mapping free-text clinical narratives to SNOMED CT concepts remains labor-intensive. We developed a large language model agent system for mapping bilingual clinical text to SNOMED CT concepts and evaluated its effect on mapping accuracy and efficiency within a human-AI collaborative workflow. We designed a three-module agent system comprising tr…
A three-module LLM agent system for bilingual clinical text expanded valid SNOMED CT candidates for expert mappers, raising hit rate@1 and R-precision while cutting per-segment mapping time by about half.
Evaluation was limited to 2,261 segments from a single tertiary academic hospital in South Korea and to bilingual clinical text, which may limit generalizability to other settings and languages.
Evidence
- Peer-reviewedJournal of Medical Systems2026-10-02
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Truvace Impact Record TRV-2026-1278, v1: “Development and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text.” Truvace, 2026-10-04. /record/TRV-2026-1278 (accessed at citation time). sha256 f8c61e658bf75764…
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