Authors: Guram Shalvovich Danelia, Nino Giorgievna Kalandadze, Levan Besarionovich Mchedlidze, Salome Iraklievna Tsereteli
Abstract: The convergence of artificial intelligence (AI) and health informatics has the potential to revolutionize clinical decision-making, disease surveillance, and personalized medicine. This study explores the integration of AI workflows with existing health informatics pipelines, examining both the transformative opportunities and the critical challenges associated with such integration. By analyzing case studies from electronic health record (EHR) systems, bioinformatics pipelines, and radiological imaging networks, we identify architectural patterns that enable seamless AI integration. Additionally, the research addresses the barriers posed by data heterogeneity, workflow fragmentation, regulatory compliance, and algorithm interpretability. The findings suggest that while AI offers immense benefits in improving healthcare outcomes and operational efficiency, a strategic, interoperable, and ethically grounded approach is necessary for scalable implementation in health informatics infrastructures.
