The NeuroVision platform
Listed in the Russian Software Registry
№19972
Core
Artificial intelligence that unifies all modules and enables seamless interaction
NeuroMed
by NeuroVision
Artificial intelligence in healthcare
An intelligent healthcare platform that improves the efficiency of clinical diagnostics and administration
Powered by technology
Challenges facing modern clinics
Administrative
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Inefficient reception processes
- Slow document processing
- Manual completion of patient forms
- Human error
- Queues
-
Navigation and safety
- Difficulty finding departments and buildings
- Incidents in corridors and wards
- Unauthorized access
- Infection control
-
Electronic document management
- Manual review of incoming and outgoing paper documents
- Disconnected administrative and clinical information systems
-
Limited appointment time
- Increasing workloads as patient volumes grow
- Significant time spent collecting routine vital signs
Diagnostic
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Heavy clinician workloads
- Clinician overload
- Declining quality of care
- Professional and emotional burnout
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Risk of diagnostic errors
Human error and limited time per patient can lead to diagnostic mistakes.
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Growing volumes of medical data
Growing data volumes require effective processing and analytics tools.
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Limited healthcare resources
Specialist shortages delay access to essential care
The NeuroMed platform
MedFace
Contactless photoplethysmography measures:
- Blood pressure
- Heart rate
- Emotional state
- Body temperature
- Blood oxygen saturation
- etc.
Additional modules
- Document recognition and AI OCR
- Video analytics and monitoring systems
- Recognition of faces, gender, age, and face coverings
- Contactless payments
Pathology detection
Ready to detect 200 conditions
- Lung and breast cancer detection
- Disease risk assessment
Key capabilities of the NeuroMed platform
AI and computer vision integration
NeuroMed combines AI and computer vision to improve diagnostic accuracy
CT/MRI image
processing
NeuroMed analyzes CT and MRI data for more accurate diagnosis
Automated medical examinations
The system automates pre-shift and periodic medical examinations, improving efficiency
Improved diagnostic accuracy
AI significantly improves the accuracy of medical reports
Reduced staff workload
Process automation helps reduce the workload of medical staff
Partnership with ANO “TsKMSCh”
Collaboration with a major Russian clinic supports the platform’s ongoing development.
MedFace — contactless diagnostics
Versatile use cases and a wide range of benefits
Contactless measurements
Measurement of heart rate, blood pressure, temperature, and oxygen saturation without physical contact
High speed
Faster routine diagnostic procedures and more time for patient interaction
Broad applicability
From modernizing medical examinations to optimizing patient pathways
Coming soon
- Stroke detection
- Detection of alcohol and drug intoxication
- Pupillometry
Lung cancer risk assessment from a single CT scan
Using a single chest CT scan, the NeuroMed model estimates the probability of lung cancer — without additional clinical data or manual annotation by a physician. It analyzes the entire scan volume and provides a calibrated 1–6-year probability together with a risk category. Results are generated in seconds and can be processed in the background on a workstation.
What the model does
-
Detects lesions
Measurement of heart rate, blood pressure, temperature, and oxygen saturation without physical contact
-
Calculates probability
Provides a calibrated probability of lung cancer over a 1–6-year horizon.
-
Assigns a risk category
Classifies the patient as low, medium, or high risk to support follow-up planning.
PERFORMANCE METRICS
- 0.83 malignancy classification accuracy (AUC)
-
0.77
prediction of a future
cancer diagnosis (AUC) -
1
CT scan
needed for assessment -
> 50K
patients with outcomes in the
training dataset
How the model works in clinical practice
The model distinguishes malignant lesions from normal and benign findings and identifies high-risk patients on earlier CT scans. It supports case triage and follow-up prioritization — assisting clinicians rather than replacing them.
-
Case triage
Prioritize scans that require a
radiologist’s attention. -
Follow-up interval
Determine repeat CT frequency: less often
for low-risk patients and more often for high-risk patients. -
Second opinion
A quantitative risk assessment to complement
the physician’s report.
Risk categories define follow-up protocols, while thresholds can be tailored to the clinic’s workload:
- Low
- routine interval
- Medium
- shorter follow-up interval
- High
- priority review and further testing
Model validation data
Validation used real-world chest CT scans collected from 2021 to 2024. Patients who underwent multiple CT scans over time were selected separately for prognostic evaluation. Physicians manually annotated all lesions into three categories.
16,111 annotated lesions
- Malignant 4,299 (26.68%)
- Benign 3,481 (21.61%)
- Other (scars, calcifications) 8,331 (51.71%)
- 2021–2024 real-world data period
- 5–14× higher early CT risk among patients subsequently diagnosed with cancer
Validation was performed on real-world but retrospective data. Cancer patients undergo repeat CT scans more frequently, so some “future diagnoses” represent recurrence rather than newly detected cancer. The results should therefore be interpreted as identifying patients with a future diagnosis and supporting follow-up. Independent prediction of cancer in initially healthy lungs still requires confirmation.
Reduce reception processing time to 30 seconds
Traditional registration takes
up to 10 minutes per patient
AI OCR implementation
- Automatic document recognition
- Transfer of all data to the hospital information system
- Preparation of all documents for signing
6× faster patient registration
AI document verification: Instant. Accurate. Borderless.
- 16,000+ types of government-issued documents
- 195+ countries and territories
-
90+
supported
languages - <1 sec for the complete document recognition and verification cycle
Platform benefits
Video analytics for clinics
-
Smart navigation
The system helps patients navigate the clinic with ease
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Access control and monitoring
- Enhanced safety and security
- Tracking staff and patient movement to optimize workflows
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Rapid response
- Automatic detection of unusual incidents in patient areas
- Alerts when personal protective equipment is missing
Platform evolution
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Early detection of lung cancer on CT and breast cancer on mammograms
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- Report generation
- Integration with hospital information systems
- Smart reception desks
-
- Cancer risk prediction 2‑5 years ahead
- Full-text report generation with an LLM
- Detection of up to 200 conditions
Outcomes
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Greater staff efficiency through the automation of routine tasks
Integration of all clinical diagnostic and administrative processes into a single system
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Improved quality of patient care and diagnostics
More accurate diagnostics and predictive AI to help prevent disease
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Optimized clinical workflows, allowing physicians to focus on patients’ needs and treatment
Adoption of medical AI innovations for patient consultations, clinical decision support, and clinic management
Future development
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The company is listed in the Russian Ministry of Digital Development’s registers of accredited IT companies and personal data operators.
Address: 12 Presnenskaya Embankment, Federation Tower, Moscow City Business Center, Moscow. The nearest metro stations are Delovoy Tsentr and Mezhdunarodnaya.