A Systematic Literature Review on the Use of Artificial Intelligence in Medical Imaging in the Diagnosis of Oncologic Pathology

Timely detection of malignant neoplasms, competent patient referral, and determination of an optimal treatment strategy remain among the most relevant issues of modern oncology. An analysis of international and domestic literature yielded the following statistics: in 2022, approximately 20 million new cancer cases and 9.7 million cancer-related deaths were recorded worldwide. In Russia, over 4.4 million patients are currently being oncological follow-up, with recent years showing a 10.5% increase in the proportion of patients observed for more than five years. A promising approach to improving the quality of tumor diagnostics is the implementation of artificial intelligence (AI) in medical imaging. One of the principal challenges in the development and training of neural network models is the acquisition of large and diverse imaging datasets, including CT series, MRI scans, and ultrasound images. The manual annotation of anatomical landmarks required to train neural networks for accurate recognition of human anatomical structures is an extremely labor-intensive but essential step in the creation of AI systems. This review analyzed recent studies by domestic and international researchers on the implementation and application of artificial intelligence (AI) in medical imaging for the diagnosis of malignant neoplasms. Studies were selected based on the following inclusion criteria: publications from 2020 to 2025, full-text literature reviews, systematic reviews, meta-analyses, original articles, and randomized controlled trials published in peer-reviewed scientific journals. Duplicates, conference abstracts, and studies lacking full text or not meeting the inclusion criteria were excluded. A total of 36 sources were included in the analysis. The review of both international and domestic literature demonstrated that AI-based systems in radiologic diagnostics of malignant neoplasms have significant potential for routine clinical use, particularly in enhancing the quality of differential diagnosis between benign and malignant lesions. Key advantages include the automation and standardization of quality control for radiographic and ultrasound images according to predefined anatomical scanning planes, as well as the ability to perform biometric measurements and detect pathognomonic features.

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