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Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Published on: December 6, 2024

Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods,

Omid Pournik1, Saadullah Farooq Abbasi1, Xuefei Ding1

  • 1Department of Electronic, Electrical and Systems Engineering, School of Engineering, University of Birmingham, Birmingham.

Studies in Health Technology and Informatics
|May 23, 2026
PubMed
Summary
This summary is machine-generated.

Large Language Models (LLMs) can automate health knowledge modeling for better data interoperability. This review shows LLMs improve semantic alignment and mapping accuracy across healthcare standards.

Keywords:
Data InteroperabilityFHIRHealthcareKnowledge ModellingLarge Language ModelsOntology Alignment

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Last Updated: May 24, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

  • Health Informatics
  • Artificial Intelligence
  • Knowledge Representation

Background:

  • Healthcare data interoperability relies on semantic alignment using standards like SNOMED CT, LOINC, and FHIR.
  • Manual ontology mapping is a bottleneck, causing delays and inconsistencies in data harmonization.

Purpose of the Study:

  • To review the application of Large Language Models (LLMs) in automating health knowledge modeling for improved data interoperability.
  • To explore how LLMs enhance or automate semantic alignment and data harmonization processes.

Main Methods:

  • A systematic literature review following PRISMA-ScR guidelines.
  • Searches conducted on PubMed and Compendex, including studies on LLM applications in ontology alignment, terminology mapping, schema integration, and knowledge-graph construction.

Main Results:

  • Twenty studies met inclusion criteria, primarily utilizing GPT-4 or BERT-derived models.
  • Methods included retrieval-augmented and embedding-based approaches.
  • Significant gains reported in automation, mapping precision, and scalability across standards like FHIR, OMOP, and UMLS.

Conclusions:

  • LLMs demonstrate substantial potential for automating semantic interoperability in health informatics.
  • Standardized evaluation benchmarks and explainable AI frameworks are crucial for the reliable adoption of LLMs in this field.