This special session focuses on artificial intelligence and computational methodologies for medical informatics, with a particular emphasis on epidemiological modeling, clinical data intelligence, and predictive analytics in biomedicine. The session uses data-driven approaches that integrate machine learning, data mining, and statistical modeling to extract clinically and biologically meaningful information from heterogeneous biomedical data. The scope includes advanced techniques for biomedical and clinical data mining, spatiotemporal analysis of health data, predictive modeling for disease progression and treatment response, and artificial intelligence-based tools to support clinical decision-making. Contributions addressing computational epidemiology, digital diagnostics, and real-world clinical data analysis are particularly encouraged. The session also welcomes research on natural language processing for biomedical texts, multimodal integration of clinical, molecular, and population-level data, and interpretable AI models to improve trust and adoption in the medical field. Within the framework of medical informatics, the session specifically addresses computational methods for genomic and evolutionary data analysis applied to infectious disease surveillance, host-pathogen interaction modeling, and real-time monitoring of pathogen genomic variability. These approaches are integrated into clinical decision support systems, digital epidemiology platforms, and public health intelligence infrastructures, enabling the translation of genomic and phylogenetic data into actionable information for clinicians and public health officials. The One Health paradigm is addressed through information systems that integrate human, animal, and environmental health data for early warning and response to epidemics. Particular attention is given to the explainability, robustness, and validation of AI models in healthcare, as well as scalable algorithms and infrastructures for large-scale biomedical and genomic data analysis. The session aims to address applications ranging from infectious diseases to public health surveillance, precision medicine, and translational biomedical research. The expected impact is the promotion of interdisciplinary research linking informatics, genetics, medicine, and public health, fostering the development of reliable, interpretable, and clinically usable AI solutions for modern medical informatics and genome-based surveillance systems.