This paper presents the concept and architecture of an intelligent system for automatic processing of medicallaboratory test results. The system integrates three key components: EasyOCR-based optical character recognitionfor extracting data from scanned forms, structured JSON storage for normalized results, and a local language model(OLM) via the Ollama framework for generating personalized medical recommendations. The application of Retrieval-Augmented Generation improves the clinical accuracy of recommendations to 4.6 out of 5.0 points. The achieved OCRaccuracy of 95.5% for numerical fields meets ISO 15189 requirements. Total processing time per form does not exceed17 seconds on CPU. The key advantage is a fully local architecture ensuring compliance with GDPR, HIPAA, and FederalLaw No. 152-FZ on personal data.
| Mualliflar | Gofurjonov , Muhammadali, Kamolov , Shamsiddin |
|---|---|
| Jurnal | Innovation science and technologiy |
| Nashr sanasi | 2026-04-01 |
| Jild | 2 |
| Son | 4 |
| Til | Ingliz |
| DOI | 10.5281/zenodo.19606581 |
DOI: 10.5281/zenodo.19606581 · Maqolaning asl sahifasi
EasyOCR, JSON, LLM, OLM, Ollama, laboratory diagnostics, artificial intelligence, NLP, CDSS, RAG, medical data, clinical decision support
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