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TRUVACE RECORD VERSION
record: TRV-2026-1278
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-04T06:57:02.511432Z
status: published
lens: g_space
sector: health
headline: Development and Validation of Human-AI Collaborative Workflow in SNOMED CT Mapping of Bilingual Clinical Text
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: reduced total mapping time by 53.9% (from 1.57 to 0.72 min per segment, including agent processing)
problem_reading: (none)
problem_evidence: (none)
quick_read: At a tertiary academic hospital in South Korea, researchers developed an LLM agent system to map bilingual free-text clinical narratives to SNOMED CT and tested it in a human-AI collaborative workflow against human-only mapping by three health information managers on 2,261 segments.

The collaborative approach matters because SNOMED CT mapping is labor-intensive but critical for semantic interoperability; the study shows accuracy gains alongside a 53.9% time reduction, though it remains uncertain how well the results transfer beyond one hospital, one language pair, and the nine categories studied.
limitation: 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.
tag: Evidence-backed gain
key_points: Study used 2,261 de-identified clinical text segments across nine clinical categories collected at a tertiary academic hospital in South Korea. | System comprised translation, abbreviation expansion, and vector-based retrieval components integrated with a pre-embedded SNOMED CT vector database. | Three health information managers compared human-only, Agent-only, and Agent-assisted human mapping using hit rate, precision, recall, F1 at k=1 and 5, and R-precision. | Modular architecture supports periodic vector database updates without retraining for bilingual terminology standardization.
rundown: Researchers built a three-module agent with translation, abbreviation expansion, and vector retrieval linked to a pre-embedded SNOMED CT database, then tested it on 2,261 de-identified bilingual segments from nine clinical categories.

Three health information managers mapped the same segments under human-only, Agent-only, and Agent-assisted conditions, with performance measured by hit rate, precision, recall, F1 at k=1 and 5, and R-precision, plus time per segment.
sources:
- peer_reviewed | Journal of Medical Systems | https://doi.org/10.1007/s10916-026-02465-3 | 2026-10-02
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