Chest X-ray and Fluorography
Detects signs of tuberculosis, pneumonia, lung cancer, suppurative and necrotic conditions. Defines pleural effusion, pneumothorax, rib fractures, and cardiomegaly
AI Algorithm detects and describes
Physician receives a pre-filled report
After AI processing
Example Report
Lungs: No focal or infiltrative changes detected
Bones: Consolidated rib fracture detected
Heart: Mediastinum enlargement. CTI = 0.6
Conclusion: No focal or infiltrative changes detected
Conclusion
No focal or infiltrative changes detected
Diagnosis: Cardiomegaly
Lungs: No focal or infiltrative changes detected
Bones: Consolidated rib fracture detected
Heart: Mediastinum enlargement. CTI = 0.6
Conclusion: No focal or infiltrative changes detected
Conclusion
No focal or infiltrative changes detected
Diagnosis: Cardiomegaly
97% Sensitivity
Validated through internal benchmarks
700,000+ Studies
Processed by the "AI Opinion" AI service
< 1 min
Average processing time per study
Use case scenarios
Retrospective analysis
Processes study archives and detects missed pathologies. Provides analytics at the facility or regional level
Normal case sorting
Separates "normal" from "pathology," prioritizes studies with deviations, optimizes the specialist's workflow, and helps focus on suspicious signs
Analyzes the study, highlights pathological signs, and generates a pre-filled report
Radiologist assistant
AI for Higher-Quality Care
03
Assesses study quality and reports technical defects
02
Automatically generates reports for "normal" studies
01
Reduces report preparation time by up to 30%
DICOM Image
AI processes the study
Integration Workflow
Returns
a secondary DICOM series with AI-visualization
a pre-filled report
a pre-filled report
Workstation
Device
Возвращается дополнительная серия с ИИ-визуализацией и предварительно заполненный протокол
PACS
Transforming Healthcare Together With our Partner
Let us help you integrate the AI service into your medical facility
FAQ
It can be deployed on-premise or in the cloud, with automatic data transfer and result return.
The server is provided by the client or the system integrator
We configure the transfer of studies from the PACS server to the AI server using standard PACS tools.
We work with all PACS that support DICOM v3.0.
We work with all PACS that support DICOM v3.0.
Compatible with any viewer or radiologist workstation that supports DICOM v3.0 and has access to the facility’s image storage/archive or a centralized subsystem
When operating within the internal network of a healthcare facility, data never leaves the perimeter — anonymization is not required.
If data is sent to an external server, anonymization is done on the client side using specialized medical data anonymization software.
If data is sent to an external server, anonymization is done on the client side using specialized medical data anonymization software.
• Technical meeting with PACS providers and project owners to design the integration architecture
• Clinical meeting with physicians and medical directors to define success metrics and assign roles
• Results are compiled into a formal pilot report based on agreed methodology
• Clinical meeting with physicians and medical directors to define success metrics and assign roles
• Results are compiled into a formal pilot report based on agreed methodology
Processing time depends on system load and hardware. On average, it takes up to 60 seconds per study after AI receives the image.
• Secondary DICOM series (DICOM SC)
• Pre-filled structured report (DICOM SR), which can be edited, copied, or automatically sent to a medical information system
• Automatic submission of AI opinion to the Health Information System as a Standardized Electronic Medical Record with a physician’s signature
• Pre-filled structured report (DICOM SR), which can be edited, copied, or automatically sent to a medical information system
• Automatic submission of AI opinion to the Health Information System as a Standardized Electronic Medical Record with a physician’s signature
Yes. The service works within the facility’s internal network, receiving studies from PACS and returning results to PACS
Improve healthcare quality with AI