Ambalavanan Radha, Snead R Sterling, Marczika Julia, Towett Gideon, Malioukis Alex, Mbogori-Kairichi Mercy
Research Department, The Self Research Institute, Broken Arrow, OK, United States.
Front Digit Health. 2025 Aug 29;7:1668385. doi: 10.3389/fdgth.2025.1668385. eCollection 2025.
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.
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.
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.
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.
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.
本体是人工智能（AI）与医疗保健之间的基础桥梁，它能构建结构化知识框架，增强数据的互操作性、临床决策支持以及精准医疗。
本观点旨在强调本体在构建适应性、可互操作框架中的关键作用，这些框架能随着技术和医学的进步而发展，以支持个性化、准确且全球互联的医疗保健解决方案。
本观点基于对PubMed、Scopus和谷歌学术进行的有针对性的文献探索，优先考虑2010年至2025年发表的研究，并在必要时纳入早期的开创性著作以提供历史背景，重点关注医疗保健领域中由本体驱动的人工智能应用。选择这些来源是因其与语义整合、互操作性和跨学科适用性相关。
通过对医学概念、关系和术语进行标准化，本体实现了跨多种医疗保健数据集（包括临床、基因组和表型数据）的语义整合。它们还解决了诸如数据碎片化和术语不一致等挑战。这种语义清晰度支持了人工智能在临床决策支持、预测分析、自然语言处理（NLP）以及针对患者的疾病建模等方面的应用。
尽管本体整合具有变革潜力，但它面临重大挑战，包括计算复杂性、可扩展性以及跨不断发展的国际标准（如SNOMED CT和HL7 FHIR）的语义不匹配。伦理问题，特别是围绕数据隐私、知情同意和算法偏差等问题，也需要仔细考虑。为应对这些挑战，本观点概述了一些策略，包括自适应本体模型、强大的治理框架以及人工智能辅助的本体管理技术。这些方法共同旨在支持个性化、准确且全球可互操作的医疗保健系统。
