A Bibliometric Analysis of AI-Driven Healthcare Literature Containing KOS Keywords: Trends, Themes, and Gaps

Abstract

As artificial intelligence (AI) becomes increasingly embedded in healthcare applications, concerns have emerged around the trustworthiness, interpretability, and context-awareness of these systems. Knowledge Organization Systems (KOS) hold considerable potential to address these challenges by supporting semantic standardization, explainability, and domain alignment. This study presents a bibliometric analysis of scholarly publications referencing both AI and healthcare concepts to examine how KOS are positioned within this evolving discourse. The findings indicate that while early literature frequently and explicitly referenced KOS—such as ontologies, controlled vocabularies, and classification systems—their visibility has declined relative to newer paradigms such as machine learning and large language models. Nevertheless, KOS-related terms remain conceptually linked to key healthcare domains, including diagnostics, therapeutics, and administration, albeit occupying a more peripheral role in the broader AI research landscape. These terms most often co-occur with topics such as natural language processing, information extraction, and the semantic enrichment of unstructured clinical data. The findings show the continued relevance of KOS in AI-healthcare discourse while highlighting the need for more deliberate alignment between KOS and emerging AI methodologies. Future work should explore frameworks that bridge conceptual presence with technical deployment. KOS may thereby offer critical contributions to the development of transparent, context-sensitive, and ethically grounded AI systems in healthcare.

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