This topic explores the principles and technologies for creating effective and intuitive interaction between humans and computer systems. The main emphasis is on developing new interface types, including brain-computer interfaces (BCI), virtual/augmented reality, and sensory/gesture-based systems that minimize cognitive load. Research in this field is critical for enhancing the accessibility, productivity, and safety of complex IT systems across all sectors, including healthcare and industry.

This direction is dedicated to utilizing powerful analytical tools and machine learning methods to process and interpret colossal medical data sets, including electronic health records, images, genomic data, and wearable device metrics. The primary goal is to uncover hidden patterns, predict disease outbreaks, optimize clinical decisions, and develop novel treatment protocols. The focus is on tools that increase diagnostic accuracy and enable the personalization of therapeutic approaches.

This topic centers on the integration of information technologies, artificial intelligence, and computer vision to automate and enhance the accuracy of medical diagnostic processes. Research includes developing intelligent systems for analyzing medical images (X-ray, MRI, CT), automated recognition of pathologies, and creating clinical decision support systems (CDSS). The objective is to reduce human error, accelerate the diagnostic process, and provide clinicians with tools for earlier and more precise disease detection.