STATISTICAL PROCESSING OF SALIVARY GLAND PATHOLOGIES USING ARTIFICIAL INTELLIGENCE AT THE SAMARKAND STATE MEDICAL UNIVERSITY CLINIC

This article explores the potential of artificial intelligence (AI) technologies in studying salivary gland pathologies at the Samarkand State Medical University clinic and their significance in statistical processing. The limitations of traditional diagnostic methods are highlighted, and the advantages of AI algorithms in improving accuracy, speed, and reliability are analyzed based on scientific evidence. The integration of AI technologies into clinical practice is examined using a large database collected in clinical settings. Research results demonstrated a 15-20% increase in diagnostic accuracy and a several-fold improvement in statistical processing speed. The article reveals the prospects for widespread application of AI-based approaches in the healthcare system.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-10-02
DOI
https://doi.org/10.5281/zenodo.17253301
Primary Topic
Cell Image Analysis Techniques
Type
article
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article

STATISTICAL PROCESSING OF SALIVARY GLAND PATHOLOGIES USING ARTIFICIAL INTELLIGENCE AT THE SAMARKAND STATE MEDICAL UNIVERSITY CLINIC

Zenodo (CERN European Organization for Nuclear Research)
Cell Image Analysis Techniques
article

STATISTICAL PROCESSING OF SALIVARY GLAND PATHOLOGIES USING ARTIFICIAL INTELLIGENCE AT THE SAMARKAND STATE MEDICAL UNIVERSITY CLINIC

article en

Abstract

This article explores the potential of artificial intelligence (AI) technologies in studying salivary gland pathologies at the Samarkand State Medical University clinic and their significance in statistical processing. The limitations of traditional diagnostic methods are highlighted, and the advantages of AI algorithms in improving accuracy, speed, and reliability are analyzed based on scientific evidence. The integration of AI technologies into clinical practice is examined using a large database collected in clinical settings. Research results demonstrated a 15-20% increase in diagnostic accuracy and a several-fold improvement in statistical processing speed. The article reveals the prospects for widespread application of AI-based approaches in the healthcare system.

Zenodo (CERN European Organization for Nuclear Research)
Samarkand State Medical Institute (UZ)
Openalex Percentile: Top 36%
Cell Image Analysis Techniques
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