Ontologies as the semantic bridge between artificial intelligence and healthcare
Radha Ambalavanan, R Sterling Snead, Julia Marczika, Gideon Towett, Alex Malioukis, Mercy Mbogori-Kairichi
Abstract
Background: Ontologies serve as a foundational bridge between artificial intelligence (AI) and healthcare, enabling structured knowledge frameworks that enhance data interoperability, clinical decision support, and precision medicine. Objective: This perspective aims to highlight the essential role of ontologies in enabling adaptive, interoperable frameworks that evolve with technological and medical advances to support personalized, accurate, and globally connected healthcare solutions. Methods: This perspective is based on a targeted literature exploration conducted across PubMed, Scopus, and Google Scholar, prioritizing studies published between 2010 and 2025 and including earlier seminal works where necessary to provide historical context, focusing on ontology-driven AI applications in healthcare. Sources were selected for their relevance to semantic integration, interoperability, and interdisciplinary applicability. Results: Through the standardization of medical concepts, relationships, and terminologies, ontologies enable semantic integration across diverse healthcare datasets, including clinical, genomic, and phenotypic data. They also address challenges such as fragmented data and inconsistent terminologies. This semantic clarity supports AI applications in clinical decision support, predictive analytics, natural language processing (NLP), and patient-specific disease modeling. Conclusions: Despite their transformative potential, ontology integration faces significant challenges, including computational complexity, scalability, and semantic mismatches across evolving international standards, such as SNOMED CT and HL7 FHIR. Ethical concerns, particularly around data privacy, informed consent, and algorithmic bias, also require careful consideration. To address these challenges, this perspective outlines strategies including adaptive ontology models, robust governance frameworks, and AI-assisted ontology management techniques. Together, these approaches aim to support personalized, accurate, and globally interoperable healthcare systems.