Botkin.AI for
Opportunities for a radiologist
Real-time data analysis
Description and localization of pathology in medical images (up to 6 minutes for CT scan, up to 1 minute for X-ray, up to 3 minutes for mammogram)
Professional DICOM viewer with convenient work tools
Tools for configuring research processing scenarios depending on the tasks, types of studies, pathologies. Access via the web interface from anywhere. Visualization of pathologies detected by artificial intelligence.
Remote description of medical images, providing a “second opinion"
The teleradiology module allows to work from any region, organize joint work of medical specialists,
further routing of research
Reduction of workload, assistance in making a medical decision
Artificial intelligence helps to identify pathologies at an early stage and reduces the percentage of errors.

Analyzed modalities

CT of the chest organs:

  • malignant neoplasms
  • syndromes associated with COVID-19
  • pulmonary emphysema
  • hydrothorax
  • pneumothorax
  • cellular lung
  • calcification of the coronary arteries


  • segmentation of pathologies in the mammary glands. Classification by ACR, BiRADS, detection of calcifications
  • recognition of lymph nodes

CT brain

  • segmentation and classification of hemorrhages
We do not believe that artificial intelligence should work without doctors. Only the correct organization of the joint work of artificial intelligence and specialists will help to achieve outstanding results in improving the efficiency and quality of X-ray diagnostics.
Our articles
Опыт разработки и внедрения системы поиска онкологических образований с помощью искусственного интеллекта
A Model-Free Comorbidities-Based Events Prediction in ICU Unit
End-to-end lung nodule detection framework with model-based feature projection block
Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans
GANs 'N Lungs: improving pneumonia prediction. Accepted to MIDL 2019 as extended abstract
Data Augmentation with GAN: Improving Chest X-Ray Pathologies Prediction on Class-Imbalanced Cases
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Research is carried out with grant support from the Skolkovo Foundation