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AI will be the new standard of care.By Lunit.

With AI,
we aim to make data-driven medicine
the new standard of care.
We are especially focused on
conquering cancer,
one of the leading causes
of death worldwide.

Happening now
at Lunit

All
Menu
News
Blog
Issue

  • News

    Lunit Secures $26M Investment from Guardant Health

  • Radiology

    A Multi-reader Study Finds Unnecessary CT Exams Can Be Reduced by 30% When Analyzing Chest Radiographs with AI

    Lunit Media • Jul 29, 2021
  • Covid19

    What do medical studies say about AI for COVID-19 management?

    Bokyeong WooBokyeong Woo • Feb 18, 2021

  • Products

    AI for Radiology and Oncology

    *Lunit lab at Samsung Medical Center, Seoul, Korea

  • Oncology

    AI-based Analysis of Cancer Tissue Predicts Response to Immunotherapy--Findings to Be Presented at ASCO 2021

    Lunit Media • Jun 3, 2021

  • Try Now

    Try using our AI online and see how our AI can help doctors

  • Evidence

    Making data-driven medicine a reality

  • Corporate

    Lunit Announces Partnership to Incorporate its Chest Detection Suite into Philips' Diagnostic X-ray Suite

    Lunit Media • Mar 4, 2021
  • Radiology

    Will AI Identify Breast Cancer Better Than Radiologists in Actual Clinical Screening?

    Lunit Media • Apr 8, 2021
See more

Research and Publications

Lunit Evidence
All
Breast
Chest
Oncology
Breast

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

Mattie Salim, MD et al.

JAMA Oncology (2020)

Chest

Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study

Se Bum Jang et al.

PLOS ONE (2020)

Breast

Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms

Thomas Schaffter, PhD et al.

JAMA Network Open (2020)

Breast

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

Mattie Salim, MD et al.

Lancet Digital Health (2020)

Chest

Validation of a Deep Learning Algorithm for the Detection of Malignant Pulmonary Nodules in Chest Radiographs

Hyunsuk Yoo et al.

JAMA Network Open (2020)

Breast

Photometric Transformer Networks and Label Adjustment for Breast Density Prediction

Jaehwan Lee1 et al.

ICCV 2019 Workshop

Chest

SRM: A Style-based Recalibration Module for Convolutional Neural Networks

HyunJae Lee1 et al.

ICCV 2019

Chest

Learning Visual Context by Comparison

Minchul Kim et al.

ECCV 2020