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Saving lives with data by providing regulatory-grade safety and effectiveness data.

Conquering cancer through AI-powered medical imaging and precision oncology diagnostics.

Lunit is a global leader in AI-based medical solutions, specifically focusing on oncology and radiology. Its technical architecture utilizes advanced deep learning and convolutional neural networks (CNNs) trained on massive, curated datasets of millions of medical images. The company offers two core product lines: Lunit INSIGHT, which focuses on chest X-rays (CXR) and mammography (MMG/DBT) to detect early-stage cancer and abnormalities with superhuman precision; and Lunit SCOPE, a breakthrough digital pathology platform that analyzes tissue slides to predict patient responses to immunotherapy. By 2026, Lunit has solidified its market position through the strategic acquisition of Volpara Health, expanding its reach into the US breast cancer screening market. Its systems are designed for high-throughput clinical environments, integrating directly with existing PACS and RIS infrastructure. Lunit's solutions are clinically validated in peer-reviewed journals, consistently demonstrating improved diagnostic sensitivity and a significant reduction in false positives, thereby optimizing workflow efficiency for radiologists and pathologists globally.
Lunit is a global leader in AI-based medical solutions, specifically focusing on oncology and radiology.
Explore all tools that specialize in detect lung nodules. This domain focus ensures Lunit delivers optimized results for this specific requirement.
Explore all tools that specialize in breast cancer screening. This domain focus ensures Lunit delivers optimized results for this specific requirement.
Prioritizes urgent cases in the radiology worklist by identifying critical findings like pneumothorax immediately upon image capture.
Uses deep learning to quantify Tumor Infiltrating Lymphocytes (TILs) and spatial distribution of immune cells within the tumor microenvironment.
Advanced analysis of 3D reconstructed breast images to identify occult lesions hidden by dense tissue.
Integration of radiology and pathology data for a unified oncology diagnostic view.
Generates probabilistic maps indicating the exact region of interest that triggered the AI's abnormality score.
Detects 10 major chest abnormalities including nodules, calcification, and consolidation with high AUC.
Monitors input image quality and alerts if image artifacts are likely to interfere with AI analysis.
Clinical consultation to determine specific diagnostic modules (CXR, MMG, or SCOPE).
Technical infrastructure audit of hospital PACS (Picture Archiving and Communication System).
Setup of Lunit Gateway for secure, encrypted DICOM transmission.
Configuration of local server or secure cloud-based processing nodes.
Integration with RIS (Radiology Information System) for automated reporting workflow.
Network configuration for DICOM C-STORE and C-FIND operations.
User acceptance testing (UAT) with lead radiologists/pathologists.
Clinical staff training on interpreting Lunit AI abnormality scores and heatmaps.
Official deployment into the clinical production environment.
Establishment of continuous monitoring and performance feedback loops.
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"Users praise the high sensitivity and seamless integration into PACS, though some mention the high cost of enterprise licensing."
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Saving lives with data by providing regulatory-grade safety and effectiveness data.

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