Machine Learning in Medicine: A New Paradigm for Diagnosis and Treatment - Posunko, Artem; Chavarha, Mykhaylo; Bashkirova, Liudmyla; Vasylyuk-Zaitseva, Svitlana; Abramchuk, Olha (Futurity Medicine, 2025)
The continuous advancement of information technologies and artificial intelligence is rapidly transforming the diagnostic, therapeutic, and prognostic capabilities of modern medicine. These developments offer unprecedented opportunities for analyzing large volumes of clinical data, which form the foundation of personalized medicine and evidence-based clinical practice. This systematic review aims to assess the current state of machine learning implementation in clinical settings, synthesize the experience of applying artificial intelligence algorithms across various medical domains, and identify promising directions for the further evolution of this innovative paradigm. Study design: A systematic analytical review was conducted using scientific literature focused on clinical and experimental studies, as well as on developed and predictive projects in the field of machine learning applications in medicine. Methodology: A comprehensive search and analysis of publications in the PubMed, Scopus, Web of Science, and Google Scholar databases over the past 15 years were performed. A total of 93 original studies were selected, comprising 65 systematic reviews and 28 meta-analyses. Content analysis, systematization, and generalization of empirical data were employed to evaluate the effectiveness of machine learning algorithms in clinical diagnostics. Results: Machine learning algorithms demonstrated high diagnostic accuracy in radiology (87–94%), pathology (91–96%), cardiology (83–89%), and oncology (88–93%). Neural networks showed significant advantages in medical image recognition, achieving diagnostic accuracy comparable to that of experienced specialists. The application of algorithms for predicting disease progression proved effective, contributing to the optimization of therapeutic strategies and a reduction in mortality rates by 12–18%. Conclusion: Machine learning is shaping a new paradigm in medical practice by enhancing diagnostic accuracy, optimizing treatment protocols, and enabling personalized therapeutic approaches. The integration of artificial intelligence technologies requires a comprehensive strategy that includes the training of healthcare professionals, the development of suitable infrastructure, and the refinement of regulatory frameworks.