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.
Information source: Vendor
Last updated: March 24, 2024

General Information

Product name Lunit INSIGHT MMG
Company Lunit
Subspeciality Breast
Modality Mammography
Disease targeted Breast cancer
Key-features Breast cancer detection, abnormality score, density assessment
Suggested use Before: adapting worklist order, flagging acute findings

Technical Specifications

Data characteristics
Population Female aged 19 years or older, screening population
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 Secondary Capture, DICOM GSPS(Grayscale Softcopy Presentation State), DICOM SR (Structured Report), HL7
Integration Integration in standard reading environment (PACS), Integration RIS (Radiological Information System), Integration CIS (Clinical Information System), Integration via AI marketplace or distribution platform, Stand-alone third party application, Stand-alone webbased
Deployment Locally on dedicated hardware, Locally virtualized (virtual machine, docker), Cloud-based
Trigger for analysis Automatically, right after the image acquisition, On demand, triggered by a user through e.g. a button click, image upload, etc.
Processing time 3 - 10 seconds


Certified, Class IIa , MDR
FDA 510(k) cleared , Class II
Intended Use Statements
Intended use (according to CE)


Market presence
On market since 06-2020
Distribution channels Various, including Blackford
Countries present (clinical, non-research use)
Paying clinical customers (institutes)
Research/test users (institutes)
Pricing model
Based on


Peer reviewed papers on performance

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

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

  • Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms (read)

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

  • Performance of a Breast Cancer Detection AI Algorithm Using the Personal Performance in Mammographic Screening Scheme (read)

  • Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening: Preliminary Study (read)

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

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

  • External Evaluation of 3 Commercial Artificial Intelligence Algorithms for Independent Assessment of Screening Mammograms (read)

Non-peer reviewed papers on performance
Other relevant papers H Nam, et al. Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography: Prediction of Tumor Invasiveness in Mammography. RSNA. 2019
S Lee, et al. Diagnostic Performances of Artificial Intelligence (AI)-based Diagnostic Support Software for Mammography: Results Using a Standardized Test Set Built for External Validation. RSNA. 2019
HE Kim, et al. Data-driven Imaging Biomarker for Breast Cancer Screening in Mammography: Early Detection of Breast Cancer. RSNA. 2019
HJ Lee, et al. Data-driven Imaging Biomarker for Breast Cancer Screening in Digital Breast Tomosynthesis: Multi-domain Learning with Mammography. RSNA. 2019
HE Kim, et al. Increase of Cancer Detection Rate and Reduction of False-Positive Recall in Screening Mammography using Artificial Intelligence: A Multi-Center Reader Study. RSNA. 2019
EK Kim, et al. Data­-driven Imaging Biomarker for Breast Cancer Screening in Mammography ­Reader Study. RSNA. 2018
S Kim, et al. Data­-Driven Imaging Biomarker for Breast Cancer Screening in Digital Breast Tomosynthesis. RSNA. 2018
EK Kim, et al. Advanced Data-Driven Imaging Biomarker for Breast Cancer Screening in Mammography. RSNA. 2017
EK Kim, et al. Applying Data-driven Imaging Biomarker in Mammography for Breast Cancer Screening. RSNA. 2016