NEURAL NETWORKS IN RADIOGRAPHY: PROSPECTS AND CHALLENGES

Golia Roman Dmitrievich

The article is devoted to the study of prospects and challenges arising from the use of neural networks in radiography. The possibilities of modern deep learning algorithms, such as convolutional neural networks (CNN), in improving the accuracy and speed of diagnosis of various diseases, including tuberculosis, pneumonia and lung cancer, are considered. Special attention is paid to the high accuracy, sensitivity and specificity of neural networks, reaching 95-98%. Despite the significant advantages, the implementation of neural networks is fraught with technical difficulties, such as high computational complexity and the need for large amounts of labeled data. Issues of interpretability of results, confidentiality of medical data, and ethical aspects related to possible discrimination and bias in training models are also discussed. Modern X-ray diagnostic devices, such as CT and C-arc, actively integrate artificial intelligence technologies that increase the accuracy and speed of diagnosis. The use of neural networks such as CNN and U-Net makes it possible to automatically detect pathologies, improve tissue segmentation and reduce the influence of the human factor. In 2025, such systems will become the standard in modern medicine, helping doctors to make timely and accurate diagnoses based on objective data and recommendations from AI.

Keywords:medicine, artificial intelligence, neural networks, healthcare, X-ray imaging, diagnostics, modern algorithms, and models