Intelligent support for medical decision-making in the diagnosis of traumatic brain injury

The article describes the author's approach to supporting medical decision-making in diagnostic studies of traumatic brain injuries (TBI). The causes and mechanisms of injury are considered. A classification based on various characteristics is carried out, and an ontological model of the subject area is developed. Based on a systems approach, a decomposition of the TBI diagnostic process is carried out. Subsystems and elements, as well as the main information flows, are identified, and the relationships between them are defined and structured. The parameters used to assess the severity of the injury are analyzed. A review of well-known literary sources devoted to the use of modern medical equipment in the diagnosis of TBI is conducted, including computed tomography (CT), magnetic resonance imaging (MRI), X-ray machines, etc. Mathematical methods and specialized software used to process primary diagnostic information are considered. It is shown that modern medical image analysis systems are trained on labeled datasets. The paper presents an analysis of methods for recognizing CT and MRI images and images from X-ray machines. The feasibility and necessity of using and implementing various prototypes and proven hardware-software medical systems for TBI diagnostics using artificial intelligence algorithms, computer vision, and neural network technologies are discussed. The integration of hardware-software medical systems and these modern technologies into healthcare requires strict regulation, standardization, and adherence to ethical principles. The basis for their implementation as a key tool for digital transformation is the state standard for artificial intelligence systems in healthcare. This state standard is designed to ensure patient safety, high standards of medical services, and minimize the risk of errors or misuse of neural networks.

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