ANONYMIZATION OF PERSONAL MEDICAL DATA BASED ON ARTIFICIAL INTELLIGENCE

The widespread digitalization of healthcare has led to the accumulation of substantial volumes of personal medical data, creating new opportunities for enhancing the quality of medical care, supporting informed clinical decision-making, and advancing scientific research. At the same time, the large-scale accumulation of personal medical data poses serious risks to cybersecurity and patient privacy. One key mechanism for mitigating these risks is the anonymization of personal medical data. However, traditional anonymization methods demonstrate significant limitations when processing complex, multidimensional, and unstructured personal medical data. This article examines approaches to using artificial intelligence methods for personal medical data anonymization. It substantiates the need to move from classical statistical, syntactic, and cryptographic models to intelligent and adaptive systems capable of automatically identifying sensitive information, performing context-sensitive transformations, and generating synthetic data. Furthermore, key technical, ethical, and regulatory issues, as well as the risks associated with the use of artificial intelligence for personal medical data anonymization, are analyzed.