Recent Advances in Leveraging AI for Scalable and Extensible Biomedical Knowledge Management
Year: 2025
Type: article
Abstract: Summary: OBJECTIVES:: : To survey the recent literature on knowledge representation and management (KRM) and their biomedical applications. Summary: METHODS: : A bibliometric analysis of KRM methodological innovations and health applications published from January 2024 to February 2025 was carried out. Summary: 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. Summary: 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.
Source: Yearbook of Medical Informatics
Authors: Gongbo Zhang, Betina Idnay, Chunhua Weng
Institution: Columbia University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
OpenAlex ID: W7225183023