Data Management and Integration

Abstract

The rapid digitization and increasing role of artificial intelligence (AI) in the healthcare sector bring to the forefront distinctive issues related to data integration and management. AI-empowered clinical processes' accuracy and dependability hinge on proper integration of healthcare data, which involves overcoming challenges such as syntactic, semantic, and technical interoperability barriers. To address such issues, healthcare organizations tend to implement standardization approaches such as HL7, FHIR, DICOM, and LOINC that enable syntactic interoperability and exchange of information without barriers. Semantic integration enhances the accuracy and consistency of AI results by further integrating the diverse datasets using ontologies and knowledge graphs. Explainable artificial intelligence (XAI) presents itself as one of the most important approaches to ensuring transparency, proper use of data, ethics, quality, as well as bias prevention and equitable healthcare provision. In addition, the use of innovative solutions such as cloud-based data lakes, ETL pipelines, and federated learning further improves secure, scalable, and interoperable data management. Therefore, the incorporation of XAI principles in data management systems in healthcare is crucial for the development of patient-centered care, data privacy compliance, and collaboration in international medical research.

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