Description
This monograph examines how artificial intelligence can improve semantic interoperability in medical healthcare. It first establishes the problem: heterogeneous health information systems exchange data whose meaning is encoded inconsistently, which limits care coordination, research and the reliability of analytics. It then reviews the standards and knowledgerepresentation foundations, including HL7 v2, CDA and FHIR, openEHR, the OMOP common data model, SNOMED CT, LOINC, ICD and RxNorm, together with ontologies and knowledge graphs. It analyses AI methods for clinical text processing, concept normalisation, terminology mapping and data harmonisation, proposes a conceptual reference architecture and maturity model, and reviews application areas and regulatory and ethical constraints in the European Union, the United States and India. It closes with evaluation methods, a research agenda and a decision guide. The central conclusion is that AI is best positioned as an accelerator and quality-control aid inside governed terminology and mapping workflows, not as an autonomous authority on clinical meaning. The published evidence supports strong performance for well-scoped normalisation and extraction tasks and growing promise for language-model assistance, but it also shows brittle behaviour, hallucinated codes and uneven generalisation across sites and populations. Claims of fully automatic semantic interoperability are therefore not supported by the evidence reviewed.