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Peer-reviewedCC BY 4.0Yearbook of Medical Informatics

Recent Advances in Knowledge Representation and Management in Healthcare: the Emerging Role of Large Language Models

  • Catalina Martínez Costa1
  • Jean Charlet2,3
  • Licong Cui4
4 affiliations
  1. 1Department of Informatics and Systems, University of Murcia, CEIR Campus Mare Nostrum, IMIB-Arrixaca, Murcia Spain
  2. 2Sorbonne Université, INSERM, Univ Sorbonne Paris Nord, LIMICS, Paris France
  3. 3AP-HP, DRCI, Paris France
  4. 4McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX USA
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Abstract

Objectives : To select and summarize the best papers published in 2024 in the field of Knowledge Representation and Management (KRM) for inclusion in the International Medical Informatics Association (IMIA) Yearbook.

Methods : We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2024.

Results : We retrieved 2,071 publications from PubMed, from which 14 were selected as candidate best papers. After in-depth evaluation, three were selected as the best papers for the KRM section. The candidate papers covered four main thematic areas: (i) Ontology Development and Application, (ii) Knowledge Graph Construction and Completion, (iii) Large Language Model (LLM)-Augmented Knowledge Representation and Reasoning, and (iv) Semantic Interoperability and Data Harmonization. Two of the selected best papers address advances in knowledge graph construction and completion, while the third focuses on semantic interoperability and harmonization across health data standards.

Conclusions : The selection process for the best papers in the KRM section for 2024 showed a wide spectrum of topics. Across several papers, it can be observed the emerging role of LLMs in enhancing ontology engineering, medical knowledge extraction, and reasoning, especially when integrated with structured clinical data and formal knowledge representations.

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Cite

BibTeXRIS
BibTeX
@article{costa2026recent,
  title = {{Recent Advances in Knowledge Representation and Management in Healthcare: the Emerging Role of Large Language Models}},
  author = {Catalina Martínez Costa and Jean Charlet and Licong Cui},
  journal = {Yearbook of Medical Informatics},
  year = {2026},
  month = nov,
  doi = {10.1055/s-0046-1816630},
  url = {https://doi.org/10.1055/s-0046-1816630},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Recent Advances in Knowledge Representation and Management in Healthcare: the Emerging Role of Large Language Models
AU  - Catalina Martínez Costa
AU  - Jean Charlet
AU  - Licong Cui
PY  - 2026
DA  - 2026/11/03
JO  - Yearbook of Medical Informatics
DO  - 10.1055/s-0046-1816630
UR  - https://doi.org/10.1055/s-0046-1816630
AB  - Objectives : To select and summarize the best papers published in 2024 in the field of Knowledge Representation and Management (KRM) for inclusion in the International Medical Informatics Association (IMIA) Yearbook.  Methods : We conducted PubMed queries and followed the IMIA Yearbook guidelines for performing biomedical informatics literature review to select the best papers in KRM published in 2024.  Results : We retrieved 2,071 publications from PubMed, from which 14 were selected as candidate best papers. After in-depth evaluation, three were selected as the best papers for the KRM section. The candidate papers covered four main thematic areas: (i) Ontology Development and Application, (ii) Knowledge Graph Construction and Completion, (iii) Large Language Model (LLM)-Augmented Knowledge Representation and Reasoning, and (iv) Semantic Interoperability and Data Harmonization. Two of the selected best papers address advances in knowledge graph construction and completion, while the third focuses on semantic interoperability and harmonization across health data standards.  Conclusions : The selection process for the best papers in the KRM section for 2024 showed a wide spectrum of topics. Across several papers, it can be observed the emerging role of LLMs in enhancing ontology engineering, medical knowledge extraction, and reasoning, especially when integrated with structured clinical data and formal knowledge representations.
SN  - 0943-4747
SN  - 2364-0502
ER  -