Vendor source

Reviewed 15 Sept 2026

Completeness Strong (67%)

Lunit INSIGHT MMG

Lunit

Lunit INSIGHT MMG is deep learning based software that assists radiologists in the interpretation of mammograms. The AI solution automatically detects suspicious areas for breast cancer on mammograms including mass, calcification, distortion and asymmetry. The analysis result contains (1) localization of suspicious areas in color or outline and (2) abnormality scores reflecting the probability that the detected area is malignant.

Supported by 31 papers

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Key Features

  • Abnormality score
  • Breast cancer detection
  • Current-prior comparison
  • Density assessment
  • Lesion type

General Information

Product name

Lunit INSIGHT MMG

Company

Modality

Mammography

Main task

Not specified

Subspeciality

Breast

Disease targeted

Breast cancer

Regulatory Information

CE Certification

Pathway:

MDR

Class:

Class IIa

Verified by Health AI Register

FDA Certification

Pathway:

510(k) cleared

Class:

Class II

FDA Number:

Verified by Health AI Register

Other certifications

Not specified

Evidence & Research

Peer-Reviewed Papers

Measuring Breast Density at Scale With AI: Insights From BreastScreen NSW

Journal of Medical Imaging and Radiation Oncology · Jul 2026

Location of AI risk markers and associated mammographic features in screening mammograms obtained years before screen-detected breast cancer

European Radiology · Jun 2026

A comparison of tumour characteristics of screen-detected and interval cancers and their relationship with breast density and AJCC prognostic stage in a UK screening cohort

European Radiology · May 2026

High risk score of breast cancer by artificial intelligence (AI) on screening mammograms: a review of negative and cancer cases

European Radiology · May 2026

Long-term prognostic implications of AI-detected versus AI-undetected breast cancers on mammography: a propensity score-matched analysis

European Radiology · Apr 2026

Performance of a Screening Mammography AI Algorithm Repurposed for Symptomatic Mammography in a Tertiary Outpatient Clinic

Diagnostics · Mar 2026

Enhancing Early Detection of Contralateral Breast Cancer in Breast Cancer Survivors Using AI-Assisted Mammography

Annals of Surgical Oncology · Mar 2026

AI-CAD for diagnostic mammography: comparison to radiologists according to different indications

European Radiology · Dec 2025

Combined use of two artificial intelligence-based algorithms for mammography triaging: a retrospective simulation study

European Radiology · Aug 2025

An Exploration of Discrepant Recalls Between AI and Human Readers of Malignant Lesions in Digital Mammography Screening

Diagnostics · Jun 2025

Clinical Application of AI in Mammography: Insights from a Prospective Study

Academic Radiology · Jun 2025

Human-AI Interaction in the ScreenTrustCAD Trial: Recall Proportion and Positive Predictive Value Related to Screening Mammograms Flagged by AI CAD versus a Human Reader

Radiology · Mar 2025

Artificial intelligence for breast cancer screening in mammography (AI-STREAM): preliminary analysis of a prospective multicenter cohort study

Nature Communications · Mar 2025

Performance of Digital Mammography-Based Artificial Intelligence Computer-Aided Diagnosis on Synthetic Mammography From Digital Breast Tomosynthesis

Korean Journal of Radiology · Feb 2025

Artificial intelligence reading digital mammogram: enhancing detection and differentiation of suspicious microcalcifications

British Journal of Radiology · Oct 2024

Artificial Intelligence Algorithm for Subclinical Breast Cancer Detection

JAMA Network Open · Oct 2024

AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study

Radiology: Artificial Intelligence · Sep 2024

Accuracy of an Artificial Intelligence System for Interval Breast Cancer Detection at Screening Mammography

Radiology · Aug 2024

Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US

Radiology · Jul 2024

Mammographic density assessment: comparison of radiologists, automated volumetric measurement, and artificial intelligence-based computer-assisted diagnosis

Acta Radiologica · Jun 2024

Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection

Breast Cancer Research · Apr 2024

Performance of an Artificial Intelligence System for Breast Cancer Detection on Screening Mammograms from BreastScreen Norway

Radiology: Artificial Intelligence · Apr 2024

Diagnostic capabilities of artificial intelligence as an additional reader in a breast cancer screening program

European Radiology · Feb 2024

Positive Predictive Values of Abnormality Scores From a Commercial Artificial Intelligence-Based Computer-Aided Diagnosis for Mammography

Korean Journal of Radiology · Feb 2024

Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms

European Radiology · Nov 2023

Artificial intelligence for breast cancer detection in screening mammography in Sweden: a prospective, population-based, paired-reader, non-inferiority study

The Lancet Digital Health · Sep 2023

Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme

Radiology · Sep 2023

External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms

JAMA Oncology · Aug 2020

Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study

The Lancet Digital Health · Aug 2020

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study

The Lancet Digital Health · Feb 2020

Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study

Scientific Reports · Feb 2018

Technical Specifications

Patient population age

Not specified

Input

Full-field digital mammogram, Synthesized 2D mammogram

Input format

DICOM

Output

Localization (color map, grayscale map, combined map, single color map), abnormality score for each lesion/side, binary assessment of abnormality, worklist order, density assessment (A,B,C and D also 1~10 score)

Output format

DICOM GSPS(Grayscale Softcopy Presentation State), DICOM Secondary Capture, DICOM SR (Structured Report), HL7

Integration

Integration CIS (Clinical Information System), Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased

Deployment

Cloud-based, Locally on dedicated hardware, Locally virtualized (virtual machine, Docker)

Trigger for analysis

Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload

Processing time

< 10 seconds

Population

Female aged 19 years or older, screening population

Market Presence

On market since

06-2020

AI Platforms

Resellers

Not specified

Countries present

Not specified

Paying clinical customers

Not specified

Research/test users

Not specified

Pricing Information

Pricing model

Not specified

Based on

Not specified

Source: Vendor | First published: Jun 9, 2024 | Last updated: Sep 15, 2026