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Use of SNOMED CT in Large Language Models: Scoping Review.

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Summary
This summary is machine-generated.

Integrating SNOMED CT with large language models (LLMs) shows promise for biomedical NLP tasks. However, varied integration methods and reporting limit definitive conclusions on effectiveness.

Keywords:
SNOMED CTknowledge graphlanguage modelslarge language modelsnatural language processingontology

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Area of Science:

  • Biomedical Natural Language Processing (NLP)
  • Artificial Intelligence in Healthcare
  • Knowledge Representation and Reasoning

Background:

  • Large language models (LLMs) excel at general NLP but face challenges in specialized biomedical domains.
  • Integrating biomedical knowledge, such as SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms), may improve LLM performance.
  • Systematic reviews on SNOMED CT integration methodologies and effectiveness in LLMs are lacking.

Purpose of the Study:

  • To examine how SNOMED CT is integrated into LLMs.
  • To identify LLM types and SNOMED CT components used in integration.
  • To assess the impact of SNOMED CT integration on LLM performance in NLP tasks.

Main Methods:

  • Conducted a scoping review following PRISMA-ScR guidelines.
  • Searched major scientific databases (ACM, ACL, IEEE, PubMed, Embase) for studies from 2018-2023.
  • Extracted and synthesized data on LLM types, SNOMED CT integration strategies, end tasks, and performance metrics.

Main Results:

  • 37 studies were included, with Bidirectional Encoder Representations from Transformers (BERT) variants being common LLMs.
  • Three integration approaches were identified: input enhancement (76%), fusion modules (14%), and external retrieval (14%).
  • Most studies reported performance improvements (89%), particularly in medical concept normalization (41%), but gains varied widely and direct comparisons were limited (51%).

Conclusions:

  • Diverse methods exist for integrating SNOMED CT into LLMs, primarily using concept descriptions.
  • While promising, inconsistent evaluation hinders definitive conclusions on effectiveness.
  • Future research needs standardized reporting, advanced integration techniques, and unified evaluation frameworks.