Analysis of the Architecture of Expert Systems for Cancer Diagnosis

The article presents an analysis of the architecture of expert systems for cancer diagnosis based on modern artificial intelligence approaches. The key components of such systems are considered, including the knowledge base, inference mechanisms, user interface, and integration with multi-omic medical data. A comparative analysis of architectural solutions used in modern expert systems is carried out, and their advantages and limitations for early diagnosis and monitoring of cancer are identified. Particular attention is paid to machine learning methods that provide automatic knowledge updating and improve diagnostic accuracy. The results obtained can be used for further development of efficient, self-learning expert systems aimed at improving the quality of medical diagnosis and monitoring of patients with cancer