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

Omid Pournik; Saadullah Farooq Abbasi; Xuefei Ding; Nasim Mahmoodi; Rose Allington; Laura Maria Peltonen; Parisis Gallos; Leila Ghalichi; Theodoros N. Arvanitis

doi:10.3233/SHTI260406

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

Omid Pournik
, Saadullah Farooq Abbasi
, Xuefei Ding
, Nasim Mahmoodi
, Rose Allington
, Laura Maria Peltonen
, Parisis Gallos
, Leila Ghalichi
, Theodoros N. Arvanitis^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

13 Downloads (Pure)

Abstract

Achieving interoperability in healthcare requires semantic alignment of data with standards such as SNOMED CT, LOINC, and FHIR. Manual ontology mapping is slow and inconsistent. Evolving Large Language Models (LLMs) can automate knowledge modelling and data harmonisation. This review explores how LLMs are applied to enhance or automate health knowledge modelling for data interoperability. Following PRISMA-ScR guidance, PubMed and Compendex were searched. Studies applying LLMs to ontology alignment, terminology mapping, schema integration, or knowledge-graph construction were included. From 166 records, 20 studies met the inclusion criteria. Most used GPT-4 or BERT-derived models with retrieval-augmented or embedding-based methods. Reported gains included automation, improved mapping precision, and scalability across standards such as FHIR, OMOP, and UMLS. LLMs show strong potential for automating semantic interoperability in health informatics. Standardised evaluation benchmarks and explainable frameworks are essential for trustworthy adoption.

Original language	English
Title of host publication	Opening the Personal Gate between Technology and Health Care
Subtitle of host publication	Proceedings of MIE 2026
Editors	Mauro Giacomini, Jaime Delgado, Theodoros N. Arvanitis, Elisavet Andrikopoulou, Arriel Benis, Gabriella Balestra, Riccardo Bellazzi, Parisis Gallos, Roberto Gatta, Daniele Roberto Giacobbe, Noemi Giordano, Maria Hägglund, Lars Lindsköld, Lenka Lhotska, Sara Marceglia, Enea Parimbelli, Lucia Sacchi, Paolo Soda, Lăcrămioara Stoicu-Tivadar, Pierangelo Veltri, Patrizia Vizza
Publisher	IOS Press
Pages	1287-1291
Number of pages	5
ISBN (Electronic)	9781643686615
DOIs	https://doi.org/10.3233/SHTI260406
Publication status	Published - 21 May 2026
Event	EFMI Medical Informatics Europe 2026: Opening the Personal Gate between Technology and Health Care - Magazzini del Cotone, Genova, Italy Duration: 25 May 2026 → 28 May 2026 https://mie2026.efmi.org/

Publication series

Name	Studies in Health Technology and Informatics
Publisher	IOS Press
Volume	336
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	EFMI Medical Informatics Europe 2026
Abbreviated title	MIE 2026
Country/Territory	Italy
City	Genova
Period	25/05/26 → 28/05/26
Internet address	https://mie2026.efmi.org/

Keywords

Health Information Interoperability/standards
Semantics
Humans
Electronic Health Records/standards
Medical Informatics/standards
Natural Language Processing
Large Language Models

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

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Pournik, O., Abbasi, S. F., Ding, X., Mahmoodi, N., Allington, R., Peltonen, L. M., Gallos, P., Ghalichi, L., & Arvanitis, T. N. (2026). Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications. In M. Giacomini, J. Delgado, T. N. Arvanitis, E. Andrikopoulou, A. Benis, G. Balestra, R. Bellazzi, P. Gallos, R. Gatta, D. R. Giacobbe, N. Giordano, M. Hägglund, L. Lindsköld, L. Lhotska, S. Marceglia, E. Parimbelli, L. Sacchi, P. Soda, L. Stoicu-Tivadar, P. Veltri, ... P. Vizza (Eds.), Opening the Personal Gate between Technology and Health Care: Proceedings of MIE 2026 (pp. 1287-1291). (Studies in Health Technology and Informatics; Vol. 336). IOS Press. https://doi.org/10.3233/SHTI260406

Pournik, Omid ; Abbasi, Saadullah Farooq ; Ding, Xuefei et al. / Large Language Models for Health Knowledge Modelling in Data Interoperability : A Scoping Review of Methods, Standards, and Applications. Opening the Personal Gate between Technology and Health Care: Proceedings of MIE 2026. editor / Mauro Giacomini ; Jaime Delgado ; Theodoros N. Arvanitis ; Elisavet Andrikopoulou ; Arriel Benis ; Gabriella Balestra ; Riccardo Bellazzi ; Parisis Gallos ; Roberto Gatta ; Daniele Roberto Giacobbe ; Noemi Giordano ; Maria Hägglund ; Lars Lindsköld ; Lenka Lhotska ; Sara Marceglia ; Enea Parimbelli ; Lucia Sacchi ; Paolo Soda ; Lăcrămioara Stoicu-Tivadar ; Pierangelo Veltri ; Patrizia Vizza. IOS Press, 2026. pp. 1287-1291 (Studies in Health Technology and Informatics).

@inproceedings{cc70ff527af6452e82e34eccc3ca1c7c,

title = "Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications",

abstract = "Achieving interoperability in healthcare requires semantic alignment of data with standards such as SNOMED CT, LOINC, and FHIR. Manual ontology mapping is slow and inconsistent. Evolving Large Language Models (LLMs) can automate knowledge modelling and data harmonisation. This review explores how LLMs are applied to enhance or automate health knowledge modelling for data interoperability. Following PRISMA-ScR guidance, PubMed and Compendex were searched. Studies applying LLMs to ontology alignment, terminology mapping, schema integration, or knowledge-graph construction were included. From 166 records, 20 studies met the inclusion criteria. Most used GPT-4 or BERT-derived models with retrieval-augmented or embedding-based methods. Reported gains included automation, improved mapping precision, and scalability across standards such as FHIR, OMOP, and UMLS. LLMs show strong potential for automating semantic interoperability in health informatics. Standardised evaluation benchmarks and explainable frameworks are essential for trustworthy adoption.",

keywords = "Health Information Interoperability/standards, Semantics, Humans, Electronic Health Records/standards, Medical Informatics/standards, Natural Language Processing, Large Language Models",

author = "Omid Pournik and Abbasi, \{Saadullah Farooq\} and Xuefei Ding and Nasim Mahmoodi and Rose Allington and Peltonen, \{Laura Maria\} and Parisis Gallos and Leila Ghalichi and Arvanitis, \{Theodoros N.\}",

year = "2026",

month = may,

day = "21",

doi = "10.3233/SHTI260406",

language = "English",

series = "Studies in Health Technology and Informatics",

publisher = "IOS Press",

pages = "1287--1291",

editor = "Mauro Giacomini and Jaime Delgado and Arvanitis, \{Theodoros N.\} and Elisavet Andrikopoulou and Arriel Benis and Gabriella Balestra and Riccardo Bellazzi and Parisis Gallos and Roberto Gatta and Giacobbe, \{Daniele Roberto\} and Noemi Giordano and Maria H{\"a}gglund and Lars Lindsk{\"o}ld and Lenka Lhotska and Sara Marceglia and Enea Parimbelli and Lucia Sacchi and Paolo Soda and L{\u a}cr{\u a}mioara Stoicu-Tivadar and Pierangelo Veltri and Patrizia Vizza",

booktitle = "Opening the Personal Gate between Technology and Health Care",

address = "Netherlands",

note = "EFMI Medical Informatics Europe 2026 : Opening the Personal Gate between Technology and Health Care, MIE 2026 ; Conference date: 25-05-2026 Through 28-05-2026",

url = "https://mie2026.efmi.org/",

}

Pournik, O , Abbasi, SF , Ding, X , Mahmoodi, N, Allington, R, Peltonen, LM, Gallos, P, Ghalichi, L & Arvanitis, TN 2026, Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications. in M Giacomini, J Delgado, TN Arvanitis, E Andrikopoulou, A Benis, G Balestra, R Bellazzi, P Gallos, R Gatta, DR Giacobbe, N Giordano, M Hägglund, L Lindsköld, L Lhotska, S Marceglia, E Parimbelli, L Sacchi, P Soda, L Stoicu-Tivadar, P Veltri & P Vizza (eds), Opening the Personal Gate between Technology and Health Care: Proceedings of MIE 2026. Studies in Health Technology and Informatics, vol. 336, IOS Press, pp. 1287-1291, EFMI Medical Informatics Europe 2026, Genova, Italy, 25/05/26. https://doi.org/10.3233/SHTI260406

Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications. / Pournik, Omid ; Abbasi, Saadullah Farooq ; Ding, Xuefei et al.
Opening the Personal Gate between Technology and Health Care: Proceedings of MIE 2026. ed. / Mauro Giacomini; Jaime Delgado; Theodoros N. Arvanitis; Elisavet Andrikopoulou; Arriel Benis; Gabriella Balestra; Riccardo Bellazzi; Parisis Gallos; Roberto Gatta; Daniele Roberto Giacobbe; Noemi Giordano; Maria Hägglund; Lars Lindsköld; Lenka Lhotska; Sara Marceglia; Enea Parimbelli; Lucia Sacchi; Paolo Soda; Lăcrămioara Stoicu-Tivadar; Pierangelo Veltri; Patrizia Vizza. IOS Press, 2026. p. 1287-1291 (Studies in Health Technology and Informatics; Vol. 336).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Large Language Models for Health Knowledge Modelling in Data Interoperability

T2 - EFMI Medical Informatics Europe 2026

AU - Pournik, Omid

AU - Abbasi, Saadullah Farooq

AU - Ding, Xuefei

AU - Mahmoodi, Nasim

AU - Allington, Rose

AU - Peltonen, Laura Maria

AU - Gallos, Parisis

AU - Ghalichi, Leila

AU - Arvanitis, Theodoros N.

PY - 2026/5/21

Y1 - 2026/5/21

N2 - Achieving interoperability in healthcare requires semantic alignment of data with standards such as SNOMED CT, LOINC, and FHIR. Manual ontology mapping is slow and inconsistent. Evolving Large Language Models (LLMs) can automate knowledge modelling and data harmonisation. This review explores how LLMs are applied to enhance or automate health knowledge modelling for data interoperability. Following PRISMA-ScR guidance, PubMed and Compendex were searched. Studies applying LLMs to ontology alignment, terminology mapping, schema integration, or knowledge-graph construction were included. From 166 records, 20 studies met the inclusion criteria. Most used GPT-4 or BERT-derived models with retrieval-augmented or embedding-based methods. Reported gains included automation, improved mapping precision, and scalability across standards such as FHIR, OMOP, and UMLS. LLMs show strong potential for automating semantic interoperability in health informatics. Standardised evaluation benchmarks and explainable frameworks are essential for trustworthy adoption.

AB - Achieving interoperability in healthcare requires semantic alignment of data with standards such as SNOMED CT, LOINC, and FHIR. Manual ontology mapping is slow and inconsistent. Evolving Large Language Models (LLMs) can automate knowledge modelling and data harmonisation. This review explores how LLMs are applied to enhance or automate health knowledge modelling for data interoperability. Following PRISMA-ScR guidance, PubMed and Compendex were searched. Studies applying LLMs to ontology alignment, terminology mapping, schema integration, or knowledge-graph construction were included. From 166 records, 20 studies met the inclusion criteria. Most used GPT-4 or BERT-derived models with retrieval-augmented or embedding-based methods. Reported gains included automation, improved mapping precision, and scalability across standards such as FHIR, OMOP, and UMLS. LLMs show strong potential for automating semantic interoperability in health informatics. Standardised evaluation benchmarks and explainable frameworks are essential for trustworthy adoption.

KW - Health Information Interoperability/standards

KW - Semantics

KW - Humans

KW - Electronic Health Records/standards

KW - Medical Informatics/standards

KW - Natural Language Processing

KW - Large Language Models

UR - https://www.scopus.com/pages/publications/105039957473

U2 - 10.3233/SHTI260406

DO - 10.3233/SHTI260406

M3 - Conference contribution

C2 - 42175079

T3 - Studies in Health Technology and Informatics

SP - 1287

EP - 1291

BT - Opening the Personal Gate between Technology and Health Care

A2 - Giacomini, Mauro

A2 - Delgado, Jaime

A2 - Arvanitis, Theodoros N.

A2 - Andrikopoulou, Elisavet

A2 - Benis, Arriel

A2 - Balestra, Gabriella

A2 - Bellazzi, Riccardo

A2 - Gallos, Parisis

A2 - Gatta, Roberto

A2 - Giacobbe, Daniele Roberto

A2 - Giordano, Noemi

A2 - Hägglund, Maria

A2 - Lindsköld, Lars

A2 - Lhotska, Lenka

A2 - Marceglia, Sara

A2 - Parimbelli, Enea

A2 - Sacchi, Lucia

A2 - Soda, Paolo

A2 - Stoicu-Tivadar, Lăcrămioara

A2 - Veltri, Pierangelo

A2 - Vizza, Patrizia

PB - IOS Press

Y2 - 25 May 2026 through 28 May 2026

ER -

Pournik O , Abbasi SF , Ding X , Mahmoodi N, Allington R, Peltonen LM et al. Large Language Models for Health Knowledge Modelling in Data Interoperability: A Scoping Review of Methods, Standards, and Applications. In Giacomini M, Delgado J, Arvanitis TN, Andrikopoulou E, Benis A, Balestra G, Bellazzi R, Gallos P, Gatta R, Giacobbe DR, Giordano N, Hägglund M, Lindsköld L, Lhotska L, Marceglia S, Parimbelli E, Sacchi L, Soda P, Stoicu-Tivadar L, Veltri P, Vizza P, editors, Opening the Personal Gate between Technology and Health Care: Proceedings of MIE 2026. IOS Press. 2026. p. 1287-1291. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI260406

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

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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