Russian-language resources for medical natural language processing (NLP) are expanding rapidly; however, their fragmentation, uneven curation, and limited clinical reliability hinder the development of safe machine learning systems for prognosis, prevention, and precision medicine. We provide the first systematic survey of Russian medical NLP datasets and analyze their suitability for clinically meaningful tasks as defined by the MedHELM taxonomy. We additionally perform expert clinical validation of three representative public corpora—RuMedPrimeData (real outpatient notes), MedSyn (synthetic clinical notes), and RuMedNLI (translated natural language inference)—assessing clinical plausibility, diagnosis accuracy, and logical consistency. Experts identified substantial reliability issues: across randomly sampled subsets of each corpus, only approximately 20% of RuMedPrimeData records, fewer than 15% of MedSyn records, and approximately 55% of RuMedNLI pairs met essential quality criteria, which can hinder downstream ML systems built on these data. To support robust applications—ranging from medical chatbots and triage assistants to predictive and preventive models—we outline practical requirements for high-quality datasets: coordinated, expert-validated, machine-readable corpora aligned with clinical guidelines and insurance logic, standardized de-identification, and transparent provenance. Strengthening these data foundations will enable the development of reliable, reproducible, and clinically relevant AI systems suitable for real-world healthcare applications.

The performance of AI solutions in mammography is largely determined by data quality, preprocessing methods, and augmentation strategies. However, systematic evaluation of these factors for models trained on aggregated multicenter datasets remains underexplored. This article presents a comparative assessment of the effects of different stages of the training pipeline on the final diagnostic accuracy. Using a pooled dataset (VinDr-Mammo, INBreast, CMMD, CBIS-DDSM), we evaluated each pipeline step—from filtering to architecture selection (EfficientNet-B3, CLIP). External testing was conducted on the MosMed database. Among the tested preprocessing steps, filtering the darkest 5% of images proved most effective. For EfficientNet-B3, optimal geometric and photometric augmentations increased test AUROC on the prepared MosMed test set from 0.844 to 0.900. Domain-specific pretraining and high resolution yielded the best performance: Mammo-CLIP achieved an AUROC of 0.949 ± 0.012, and EfficientNet-B3 reached 0.934 ± 0.013. Overall, this study developed a standardized pipeline that includes sequential data filtering and harmonization, augmentation optimization, and architecture selection. This approach ensures reliable and reproducible results for automated mammogram classification.