PREDICTIVE AND PRECISION MEDICINE: NEW HORIZONS IN HEALTH ASSESSMENT AND PREDICTION OF PATHOLOGICAL PROCESSES
DOI:
https://doi.org/10.11603/mie.1996-1960.2025.1-2.15987Keywords:
predictive medicine, precision medicine, preventive medicine, health assessment, prediction of pathological processes, personal wearable devices, data imputation, artificial intelligence, big dataAbstract
Background. Modern healthcare systems are undergoing a significant transformation driven by the development of predictive, precision, preventive, and personalized medicine. These approaches represent a transition from a traditional reactive model of healthcare to a proactive model focused on early risk detection, disease prevention, and individualized treatment strategies based on biological and behavioral characteristics of patients.
Materials and Methods. The study was conducted using analytical review and synthesis of contemporary scientific literature devoted to predictive, precision, and preventive medicine.
Particular attention was given to the role of biomedical technologies, wearable medical devices, digital health systems, and artificial intelligence in health assessment and prediction of pathological processes. The analysis also considered challenges related to data completeness, missing laboratory results, and statistical and machine-learning approaches to data imputation.
Results. The findings indicate that modern biomedical technologies, including genomic sequencing, proteomics, big data analytics, wearable sensors, and Internet of Things technologies, provide new opportunities for individualized health monitoring and early detection of pathological processes.
Wearable medical devices enable continuous physiological monitoring, screening, and detection of deviations from normal parameters. However, the increasing use of digital medical technologies generates significant challenges associated with data quality, missing values in clinical datasets, and the need for reliable statistical and algorithmic imputation methods. In addition, the rapid development of artificial intelligence and digital health infrastructures raises important ethical, organizational, and technological issues, including data confidentiality, interoperability, and healthcare inequality.
Conclusions. Predictive and precision medicine represent an emerging paradigm of healthcare that integrates biomedical technologies, digital monitoring systems, and mathematical modeling to improve health assessment and disease prediction. Future research should focus on improving methods for individualized patient characterization, developing reliable predictive models, ensuring data quality, and creating open and ethically responsible healthcare information systems. Effective implementation of these strategies also requires improved training of healthcare professionals and the development of interdisciplinary approaches combining medicine, data science, and digital technologies.
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