PERSPECTIVE article
Front. Digit. Health
Sec. Health Informatics
Volume 7 - 2025 | doi: 10.3389/fdgth.2025.1668385
This article is part of the Research TopicPushing Boundaries in EHR Implementation and Innovation through Advanced Digital Health TechnologiesView all 4 articles
Ontologies as the Semantic Bridge Between Artificial Intelligence and Healthcare
Provisionally accepted
- The Self Research Institute, Broken Arrow, United States
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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.
Keywords: ontologies, Semantic interoperability, artificial intelligence, Healthcare integration, Clinical decision support, Natural Language Processing, Ethical AI, Multi-modal data integration
Received: 17 Jul 2025; Accepted: 18 Aug 2025.
Copyright: © 2025 Ambalavanan, Snead, Marczika, Towett, Malioukis and Mbogori-Kairichi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Radha Ambalavanan, The Self Research Institute, Broken Arrow, United States
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