Clinical Decision Support V.4.2

AI-powered software for faster and more accurate breast cancer diagnosis

Artificial Intelligence platform for automated mammography interpretation using advanced decision-support and analytical models to support radiologists in early breast cancer detection and clinical decision-making.

Early Detection

Early breast cancer detection

Mammography AI

AI-powered mammography analysis

Lesion Assessment

Accurate lesion assessment

Clinical Support

Fast clinical decision support

See How It Works View Platform Specs

ANOMALY: 98.7%

LESION: 87.4%

STATUS: ACTIVE ANALYSIS

FRAME: 4892.X

X: 142.22 Y: 89.15

Global Health Alert

Breast Cancer Statistics

Understanding the scale and urgency of breast cancer screening globally.

Prevalence

Most common cancer diagnosed among women worldwide.

Annual Diagnoses

2.3M+

New breast cancer cases detected globally every year.

Annual Mortality

670K+

Lives lost annually due to late diagnosis or lack of screenings.

donut_large Gender Distribution

99% Female

Women 99%

Men 1%

Although breast cancer primarily affects women, it can occasionally occur in men.

insights 5-Year Survival Rates

99%

Early Stage Localized

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30%

Late Stage Distant Metastasis

Early screening allows anomalies to be caught before spreading, increasing survival likelihood up to 99%.

All statistics verified by the World Health Organization (WHO) Global Cancer Observatory. WHO Breast Cancer Fact Sheet open_in_new

MAMMOGRAM SCAN ID: CANCER-REF-001

Density: HIGH (Type C)

Lesion Detected: Q3

ACCURACY PROBABILITY: 98.7% BI-RADS CATEGORY: 4

Breast Cancer Overview

Breast cancer is the most common cancer among women and one of the leading causes of cancer-related mortality worldwide. Early detection through routine mammography significantly improves survival rates and treatment outcomes.

Digital mammography remains the gold standard for screening. However, breast density, subtle lesion appearance, and variability in image interpretation may reduce diagnostic accuracy.

AIscan enhances breast cancer screening by automatically analyzing mammography images, identifying suspicious lesions, providing malignancy risk assessment, and supporting radiologists with faster and more consistent clinical decisions.

The Problem

Challenges vs. AIscan Solution

How our automated decision support system addresses traditional hurdles in mammography screening.

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Qualification Gap

Variability in radiologists' experience may affect diagnostic consistency.

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Standardized Precision

Delivers consistent, uniform automated analysis to support and align radiological assessments.

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High Workload

The increasing volume of mammography examinations places significant pressure on radiologists.

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Accelerated Workflow

Dramatically reduces evaluation times by automatically flagging anomalies and pre-sorting cases.

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Human Eye Limitation

Dense breast tissue and subtle lesions may be difficult to detect by visual assessment alone.

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Visual Contrast Support

Employs pixel-level enhancement and pattern recognition to identify hidden lesions in dense tissue.

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Lack of AI Support

Many healthcare facilities still lack automated AI-assisted mammography analysis and clinical decision support.

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Intelligent Double-Reading

Functions as a digital second reader, providing malignancy risk calculations and BI-RADS classification.

Clinical Workflow Integration

Designed to augment, not replace, the diagnostic process. Seamlessly integrated from capture to follow-up.

AiScan Clinical Workflow Diagram

STEP 01

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Mammography

2D high-resolution scan is captured and securely ingested.

STEP 02

psychology

Our Tool

AI analyzes and flags suspicious lesions in real-time.

STEP 03

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Treatment

Data-driven treatment plan formulated by clinical team.

STEP 04

monitoring

Monitoring

Continuous monitoring utilizing baseline AI metrics.

Traditional vs. AiScan Workflow

Comparing the standard diagnostic process with our AI-augmented clinical pipeline.

history Traditional Approach

Standard mammography review relies entirely on manual analysis. This process can be slow, cognitively taxing for radiologists, and susceptible to human error, particularly when identifying subtle, early-stage anomalies across hundreds of daily scans.

bolt AiScan Augmented Workflow

Our platform integrates seamlessly to provide real-time AI detection. By highlighting suspicious regions instantly, we reduce cognitive load, dramatically increase diagnostic confidence, and improve overall clinical efficiency without disrupting the established treatment pipeline.

layers Solutions Stack

AIscan Platform

AI-powered clinical decision support platform for automated mammography analysis and early breast cancer detection.

featured_play_list Key Features

checkAutomated mammography analysis

checkBreast lesion detection and localization

checkAI-assisted BI-RADS classification

checkMalignancy probability estimation

checkBreast density assessment

checkAutomated pattern analysis

checkClinical decision support

checkAutomated reporting

checkDICOM compatibility

checkMulti-center cloud access

SECURITY: HIPAA & GDPR COMPLIANT DICOM 3.0 PROTOCOLS

Research Laboratory

AISCAN LAB

AIscan Lab develops artificial intelligence solutions for breast imaging and precision oncology. Our multidisciplinary team combines expertise in radiology, oncology, artificial intelligence, and medical image analysis.

Mission

To improve women's health through earlier, faster, and more accurate breast cancer diagnosis using artificial intelligence.

Vision

To become a leading global provider of AI-powered breast imaging solutions.

handshakeAcademic & Clinical Partners

The platform is being developed in clinical collaboration with Uzbekistan's premier research institutes:

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Tashkent State Medical University

Clinical validation and diagnostic testing protocols.

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Research Institute for the Development of Digital Technologies and Artificial Intelligence

Development and optimization of decision-support and analytical models.

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Republican Specialized Scientific and Practical Medical Center of Oncology and Radiology

Dataset collection and oncology integration feedback.

scienceClinical Research & Validation

AIscan algorithms are developed and validated using rigorous medical methodologies:

circleLarge-scale anonymized mammography datasets
circleInternational BI-RADS guidelines and compliance
circleMulti-center clinical validation and feedback
circleAlgorithmic architectures tuned for diagnostic imaging
circleAutomated pattern analysis & features extraction
circleAdvanced analytical methodologies for transparent outputs

analyticsHealthcare Impact

Integrating AIscan into standard radiological workflows produces immediate and measurable benefits:

checkEarlier diagnosis of subtle breast abnormalities
checkHigher diagnostic confidence for medical staff
checkReduced interpretation variability among clinical sites
checkImproved screening efficiency and reduced workload
checkBetter patient outcomes and early interventions
checkLower breast cancer mortality globally