Ontologies as the semantic bridge between artificial intelligence and healthcare

Area of Science:

• Healthcare Informatics
• Artificial Intelligence
• Medical Ontologies

Background:

• Ontologies bridge AI and healthcare, creating structured knowledge for better data interoperability, clinical decision support, and precision medicine.
• They are foundational for developing adaptive, interoperable healthcare frameworks that align with medical and technological progress.

Purpose of the Study:

• To highlight the critical role of ontologies in advancing AI-driven healthcare.
• To explore how ontologies facilitate personalized, accurate, and globally connected healthcare solutions.

Main Methods:

• A targeted literature review was conducted across PubMed, Scopus, and Google Scholar.
• Studies published between 2010 and 2025 were prioritized, with earlier seminal works included for historical context.
• Focus was placed on ontology applications in AI for healthcare, emphasizing semantic integration and interoperability.

Main Results:

• Ontologies standardize medical concepts, enabling semantic integration of diverse datasets (clinical, genomic, phenotypic).
• They enhance AI applications, including clinical decision support, predictive analytics, natural language processing (NLP), and patient-specific disease modeling.
• Ontologies address data fragmentation and inconsistent terminologies, improving data clarity.

Conclusions:

• Ontology integration faces challenges: computational complexity, scalability, semantic mismatches with standards (SNOMED CT, HL7 FHIR), and ethical concerns (privacy, bias).
• Strategies like adaptive models, governance frameworks, and AI-assisted management are proposed to overcome these hurdles.
• These advancements aim to foster personalized, accurate, and globally interoperable healthcare systems.