Recent Advances in Leveraging AI for Scalable and Extensible Biomedical Knowledge Management
- 1Columbia University, New York, NY USA
Abstract
Objectives: : To survey the recent literature on knowledge representation and management (KRM) and their biomedical applications.
Methods : A bibliometric analysis of KRM methodological innovations and health applications published from January 2024 to February 2025 was carried out.
Results : Four major methodological development trends emerge from our analysis, which are multimodal data integration, ontology-driven knowledge representation, continuous knowledge representation learning, and contrastive learning for knowledge extraction. These methods are applied across a range of biomedical domains, as represented by accelerated drug development, clinical decision support, and personalized medicine.
Discussion : There is a growing emphasis on data-driven and learning-based approaches to biomedical knowledge representation. However, challenges remain in developing standardized evaluation benchmarks and enabling continuous updates to knowledge resources. Future innovations may arise from integrating knowledge across overlapping domains—such as precision medicine and drug repurposing—to enhance the translational impact of KRM methods.
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BibTeX
@article{zhang2026recent,
title = {{Recent Advances in Leveraging AI for Scalable and Extensible Biomedical Knowledge Management}},
author = {Gongbo Zhang and Betina Idnay and Chunhua Weng},
journal = {Yearbook of Medical Informatics},
year = {2026},
month = nov,
doi = {10.1055/s-0046-1816628},
url = {https://doi.org/10.1055/s-0046-1816628},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Recent Advances in Leveraging AI for Scalable and Extensible Biomedical Knowledge Management
AU - Gongbo Zhang
AU - Betina Idnay
AU - Chunhua Weng
PY - 2026
DA - 2026/11/03
JO - Yearbook of Medical Informatics
DO - 10.1055/s-0046-1816628
UR - https://doi.org/10.1055/s-0046-1816628
AB - Objectives: : To survey the recent literature on knowledge representation and management (KRM) and their biomedical applications. Methods : A bibliometric analysis of KRM methodological innovations and health applications published from January 2024 to February 2025 was carried out. Results : Four major methodological development trends emerge from our analysis, which are multimodal data integration, ontology-driven knowledge representation, continuous knowledge representation learning, and contrastive learning for knowledge extraction. These methods are applied across a range of biomedical domains, as represented by accelerated drug development, clinical decision support, and personalized medicine. Discussion : There is a growing emphasis on data-driven and learning-based approaches to biomedical knowledge representation. However, challenges remain in developing standardized evaluation benchmarks and enabling continuous updates to knowledge resources. Future innovations may arise from integrating knowledge across overlapping domains—such as precision medicine and drug repurposing—to enhance the translational impact of KRM methods.
SN - 0943-4747
SN - 2364-0502
ER -