MRI scans were obtained of the cervical section of the spinal cords of 30 patients with remitting multiple sclerosis. During the study period, patients received immunomodulatory agents (seven received interferon beta-1a, 13 received interferon beta-1b, and 10 received glatiramer acetate). Total focus volume in brain matter was assessed before and after treatment, along with the linear size of the spinal cord on sagittal sections at the level of the inferior margin of the body of C2. There was a significant (p = 0.002) reduction in focus volume in the group overall, from 10993 mm(3) (8098-13888 mm(3), p< 0.05; Me = 9336) to 5630 mm(3) (7400-3860 mm(3), p < 0.05, Me = 4180). There were also significant decreases in focus volume on the background of treatment with interferon beta-1b and glatiramer acetate (p = 0.026 and 0.027, respectively). Significant differences between groups were found in the magnitudes of increases in spinal cord atrophy: H (2, n = 30) = 8.06; p = 0.0178. Patients given glatiramer acetate showed a significantly smaller increase in atrophy as compared with those treated with interferon beta (p < 0.02).

Relevance. The current state of medicine is imperfect as in every other field. Some main discrete problems may be separated in diagnostics and disease management. Biomedical data operation difficulties are a serious limiting factor in solving crucial healthcare problems, represented in the statistically significant groups of diseases. Accumulation of life science data creates as possibilities as challenges to effectively utilize it in clinical practice. Machine learning-based tools are necessary for the generation of new insights and the discovery of new hidden patterns especially on big datasets. AI-based decisions may be successfully utilized for diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. Objective. To analyze the potential of machine learning algorithms in healthcare on exact existing problems and make a forecast of their development in near future. Method. An analytical review of the literature on keywords from the scientometric databases Scopus, PubMed, Wiley. Search depth 7 years from 2013 to 2020. Results. Analyzing the current general state of the healthcare system we separated the most relevant problems linked to diagnostics, treatment, and systemic management: diagnostics errors, delayed diagnostics (including during emergencies), overdiagnosis, bureaucracy, communication issues, and "handoff" difficulties. We examined details of the convenient decision-making process in the clinical environment in order to define exact points which may be significantly improved by AI-based decisions, among them: diagnosis of diseases, monitoring of general health, prediction of risks, treatment solutions, and biomedical knowledge generation. We defined machine learning algorithms as a prospective tool for disease diagnostics and management, as well as for new utilizable insights generation and big data processing. Conclusion. Machine learning is a group of technologies that can become a cornerstone for dealing with various medical problems. But still, we have some problems to solve before the intense implementation of such tools in the healthcare system.

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