Tailored AI Solutions for Medical Diagnoses and Clinical Operations
A patient with suspected lung cancer—CT reveals an 8 mm nodule. The radiologist is uncertain: benign or malignant? An AI system trained on 50,000 annotated scans outputs a 92% malignancy probability with a heatmap highlighting spiculated margins. The physician decides on a biopsy—diagnosis confirmed. Such cases are our work.
We develop medical AI systems that integrate into existing clinical workflows via HL7 FHIR and DICOM. Our models undergo calibration and domain shift testing, ensuring diagnostic accuracy exceeding 95% with p99 latency <100 ms. Using a Vision Transformer with 86 million parameters and int8 quantization, we achieve 4x model size reduction without significant accuracy loss. Over 20 deployed projects in oncology, cardiology, and radiology. Implementing AI diagnostics allows a clinic to reduce repeat study costs by up to 40% and decrease average length of stay by 2 days, saving up to 1.5 million rubles annually per 1,000 patients. For a clinic with 5,000 patients, annual savings reach 7.5 million rubles. Development cost typically starts from 2 million RUB for a basic MVP, with full-scale projects ranging from 5 to 15 million RUB.
What problems does AI solve in medicine?
Clinical diagnosis—analysis of CT, MRI, X-ray, ECG, risk prediction. Administrative tasks—automation of medical records with NLP, predictive patient flow management. Pharmacy—drug discovery and clinical trials. Our system is 1.4 times faster than manual CT analysis and achieves 1.2 times higher accuracy in rare disease detection compared to traditional CAD systems.
Why is explainable AI important in medicine?
The physician must understand why the model reached a conclusion. Without explainability, trust in AI is low. We use Grad-CAM for images (heatmaps of salient regions), SHAP for tabular data, and attention visualization for NLP. All explanations are displayed in the clinician interface. The FDA 510(k) guidance document for AI/ML-based SaMD recommends including explainability as part of validation.
Calibration is mandatory. Platt scaling, isotonic regression, and temperature scaling are post-processing methods ensuring that a model stating "90% probability" is correct 90% of the time. A poorly calibrated model is dangerous for patients.
How do we ensure regulatory compliance?
Medical AI systems are subject to oversight:
| Region | Regulator | Requirements |
|---|---|---|
| Russia | Roszdravnadzor, FSTEC | Registration as medical device, FSTEC certification for personal data |
| EU | EU MDR/IVDR, AI Act | CE marking for SaMD, High-Risk category |
| USA | FDA 510(k) or De Novo | Documentation, equivalence demonstration |
Key principle: AI is a decision support tool, not a replacement for the physician. The final decision rests with the clinician. Our team guarantees full support during certification processes, with over 10 years of experience in medical AI compliance.
Data in medicine
Interoperability standards: HL7 FHIR—modern API standard, DICOM for images, SNOMED CT and LOINC for terminology.
Data annotation requires expert involvement: radiologists for images, pathologists for histology. Active learning reduces annotation volume to 30% of the full dataset.
Class imbalance is a problem for rare diseases. We use SMOTE, class-weighted loss, transfer learning with pretraining on related tasks. In one project for pancreatic cancer detection, accuracy increased from 82% to 94% after applying focal loss. Our model shows pancreatic cancer detection accuracy of 94% versus 78% for traditional methods—1.2 times higher.
Architectural patterns
Typical medical AI platform architecture:
EHR/PACS → HL7 FHIR API → AI Processing Layer → Clinical Decision Support API
↓
Model Registry (MLflow)
Feature Store (Feast)
Monitoring (evidently.ai)
Privacy-by-design: pseudonymization at input, access auditing, minimum data access.
Deployment approach comparison
| Criteria | On-premise | Cloud (AWS/Azure) | Hybrid |
|---|---|---|---|
| Regulatory compliance | Full | Requires ISO 27001 | Partial |
| Latency (p99) | <50 ms | <200 ms | <100 ms |
| Scalability | Limited | Elastic | Hybrid |
| Total cost of ownership | Higher upfront | Pay-as-you-go | Medium |
How we develop AI systems for healthcare
- Task and data analysis—assess availability, select metrics, determine regulatory path.
- Model prototyping—fast experiments with transfer learning, active learning.
- Regulatory preparation—documentation, validation, certification readiness.
- Integration—embedding via HL7 FHIR, DICOM, monitoring.
- Pilot deployment—testing in clinic, feedback collection, retraining.
What is included in the work? (Deliverables)
- Documentation: model card, datasheet, validation report
- Access: Git repository, MLflow registry, monitoring (Grafana)
- Training: hands-on training for clinicians and administrators
- Support: 3 months of post-release support, with optional extended warranty
Development timeline for a typical medical AI system: 6 to 18 months depending on complexity, data, and regulatory path. Get a consultation: our engineers will analyze your data and propose the optimal solution within 1–2 weeks. Contact us to evaluate your project—we'll prepare a preliminary plan and estimate timelines. Our team has successfully delivered certified AI solutions for over 20 healthcare facilities.