Abstract
Modern biomedical engineering is characterized by the rapid growth of data volumes that require processing and analysis to support clinical decision-making. Information technology plays a key role in ensuring the high-performance processing of these large datasets, contributing to the increased accuracy and speed of clinical diagnoses, as well as more effective subsequent patient treatment. This article aims to review current approaches and technologies used for biomedical data processing and to rethink the approach to using big data in decision support systems. Special attention is given to machine learning methods that enhance data analysis efficiency. The data processing approach proposed in this article allows for an 10-12% increase in the accuracy of spinal pathology classification, confirming its feasibility in medical practice.