Artificial Intelligence in Medicine
Artificial intelligence is transforming healthcare — from automating routine documentation to supporting clinical decisions. Lissa Health applies AI across several key areas while keeping the physician as the final decision-maker.
Medical Document Recognition (OCR + NLP)
Most medical data exists in unstructured form: PDF files, photos of forms, handwritten prescriptions. Lissa Health combines optical character recognition (OCR) and natural language processing (NLP) to extract structured data:
- biomarker names and values;
- units of measurement and reference ranges;
- test dates;
- diagnoses and physician conclusions.
Extraction accuracy exceeds 90% on standard lab report formats. The system processes documents in Russian and English, adapting to different laboratory layouts.
Semantic Decomposition
After OCR, text undergoes semantic decomposition — parsing into atomic medical facts mapped to international terminologies (LOINC for lab tests, SNOMED CT for clinical findings). This allows comparing "glucose" from one lab with "Glucose" from another — the system understands they are the same marker.
Predictive Analytics
Based on accumulated biomarkers, Lissa Health builds:
- trend charts — visualization of value changes over time;
- health index — aggregated assessment by body systems;
- biological age — estimation using validated models (PhenoAge);
- AI reports — text summaries explaining deviations in plain language.
Models predict risks (mortality risk, metabolic risk), not diagnoses. Any anomaly is recommended for discussion with a healthcare provider.
Clinical Decision Support
Lissa Health does not replace physicians but reduces routine workload:
- automatic chart population from uploaded documents;
- summary of marker changes before appointments;
- secure data sharing with clinicians via temporary links.
Platform data shows AI services save up to 25% of clinician time on documentation.
Limitations and Responsibility
AI in medicine has known limitations:
- recognition quality depends on source document quality;
- models are trained on population data and may require calibration;
- the system is not intended for emergency diagnosis.
Lissa Health is positioned as an information service for storing, structuring, and analyzing data — not as a medical device for diagnosis.