Purpose: to study aspects of implementing artificial intelligence in oncology diagnostics, in particular, unique approaches to automating medical data analysis and increasing the accuracy of early detection of pathologies. Results. It was confirmed that the use of artificial intelligence significantly improves diagnostic efficiency by increasing the accuracy of detecting abnormalities in images and clinical records. At the same time, artificial intelligence reduces data processing time and the risks associated with the human factor. The main obstacles to implementation remain insufficient data standardisation, limited technical infrastructure, and ethical and legal issues related to patient safety and liability for possible errors. Conclusion. The study’s findings confirm the significant potential of artificial intelligence in transforming cancer diagnostics, in particular by automating analysis and individualising treatment approaches. Prospects for further research are to improve algorithms to minimise the required computing resources, expand access to high-quality data, and create integrated platforms that combine artificial intelligence with other technologies, which will contribute to more accurate forecasting and improve the quality of medical services.