Large Language Models in Bio-Ontology Research: A Review

Biomedical ontologies are critical for structuring domain knowledge and enabling integrative analyses in the life sciences. Traditional ontology development is labor-intensive, requiring extensive expert curation. Recent advances in artificial intelligence, particularly large language models (LLMs), have opened new possibilities to automate and enhance various aspects of bio-ontology research. This review article synthesizes findings from recent studies on LLM-assisted ontology creation, mapping, integration, and semantic search, while addressing challenges such as bias, reliability, and ethical concerns. We also discuss promising future directions and emerging trends that may further transform the way biomedical ontologies are developed, maintained, and used.