Twenty studies validate AI performance across diverse populations and clinical settings, including award-winning research in emergency radiology

SEOUL, South Korea, November 25, 2024 — Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced its participation in the Radiological Society of North America (RSNA) 2024 Annual Meeting, presenting a record-breaking 20 abstracts. As RSNA 2024 highlights the transformative role of AI in radiology, Lunit’s groundbreaking research and solutions emphasize how AI can address critical challenges in healthcare, from improving diagnostic accuracy to enhancing clinician efficiency.

Among Lunit’s studies, two groundbreaking research presentations stand out:

1. Enhancing Breast Cancer Detection Across Diverse Populations

Presented by Dr. Hari Trivedi from Emory University, the study focuses on Lunit INSIGHT DBT, an AI-powered solution for breast cancer detection using digital breast tomosynthesis (DBT) images. Conducted on a large, racially heterogeneous screening population from Emory University (137,460 cases from 2013-2020), the research demonstrated Lunit AI's consistent performance across various subgroups, including race, ethnicity, age, and breast density.

Key findings include:

● Robust overall AUC of 0.920

● High sensitivity (84.5%) and specificity (83.8%)

● Consistent performance across lesion types, from calcifications to architectural distortions

● Demonstrated the reliability and potential of Lunit INSIGHT DBT to address disparities in breast cancer detection, underscoring its capability to serve as a robust tool for diverse patient populations globally

2. Empowering Clinicians Through AI-Assisted Chest X-Ray Interpretation

Presented by Dr. Ruchir Shah from Oxford University Hospitals and awarded the Trainee Research Prize - Resident, this study evaluated the impact of Lunit INSIGHT CXR, an AI-powered solution for chest X-ray interpretation, on clinician performance in emergency and inpatient care settings.

The study involved 30 clinicians from various specialties and experience levels, who interpreted 500 chest X-rays with and without AI assistance on the RAIQC platform.

Notable results include:

● AI demonstrated superior standalone performance with AUCs of 0.83-0.99 across 10 pathologies, with exceptional accuracy (AUC>0.9) in 8 pathologies

● Significant improvement in clinicians' accuracy in 8 out of 10 pathologies with AI assistance, including pulmonary nodules, pleural effusion, and fibrosis

● Marked greatest improvement in fibrosis detection with a delta AUC of 0.193

● Underscored Lunit INSIGHT CXR's potential to empower clinicians in making accurate and timely decisions, improving both efficiency and patient outcomes in high-pressure clinical environments, addressing growing diagnostic demands in hospitals worldwide

“RSNA 2024’s focus on AI in radiology aligns seamlessly with Lunit’s mission to conquer cancer through AI," said Brandon Suh, CEO of Lunit. "The studies we’re presenting this year exemplify how our AI solutions address real-world challenges—ensuring diagnostic equity in breast cancer detection and empowering clinicians with improved accuracy for critical chest X-ray findings. These innovations are about reshaping the standard of care for diverse patient populations globally. We are honored to contribute to this year’s discussion on AI’s role in radiology and look forward to sharing how Lunit is driving meaningful impact in the field.”

The RSNA 2024 Annual Meeting will take place in Chicago, Illinois, from December 1-6, 2024. For more information, visit Lunit’s booth #4929 in the AI Showcase.

Presentations at RSNA 2024 featuring the Lunit INSIGHT suite include:

Lunit INSIGHT MMG, “AI Software Performance in Unliteral Mammography: Simulating Total Mastectomy Scenarios” | S3A-SPBR-6
Lunit INSIGHT MMG, “Real-World Impact of AI CAD in Population-Based Breast Cancer Screening - Comparing Screening Metrics Before and After the ScreentrustCAD Trial” | S5-SSBR02-3
Lunit INSIGHT MMG, “Early Alerts - an Analysis of Temporal Changes in Three Mammography-Based Artificial Intelligence Algorithm Scores Over the Course of a Patient’s Screening Timeline” | S5-SSBR02-5
Lunit INSIGHT MMG, “Enhancing Mammography Screening: a Comparative Analysis of AI Ensemble Strategies” | M1-SSBR04-4
Lunit INSIGHT MMG, “Artificial Intelligence (AI)-Based Mammography Scores for Predicting Lymph Node Metastasis in Early-Stage Breast Cancer” | M5A-SPBR-6
Lunit INSIGHT MMG, “Feature Analysis of Screening Detected Cancer and Missed Cancer of Artificial Intelligence-Based Computer-Assisted Diagnosis (AI-CAD) on AI-Stream Study” | M5A-SPBR-7
Lunit INSIGHT MMG, “External Multi-Center Multi-Manufacturer Validation of a Mammography-Based AI Score to Select Patients for Supplemental Breast Cancer Screening” | M5A-SPBR-8
Lunit INSIGHT MMG, “Comparison of the Risk Score for Cancer Detection on Screening Mammograms Given by an AI Algorithm and the Radiologists” | M5A-SPBR-10
Lunit INSIGHT MMG, “Using Artificial Intelligence (AI) Scores for Mammography Interpretation in Predicting Recurrence After DCIS Treatment” | R1-SSBR10-1
Lunit INSIGHT MMG, “AI Score on Screening Mammograms by Time” | T7-SSBR07-2
Lunit INSIGHT MMG, “Artificial Intelligence Mammography Interpretation Systems are More Affected by Mammographic Image Quality Issues than Radiologists” | T7-SSBR07-3
Lunit INSIGHT MMG, “Post-Market Surveillance of AI in a Breast Cancer Screening Setting” | M2-SPBR-9
Lunit INSIGHT MMG, “Combining Two or Three AI CAD Systems to Replace Radiologist Double-Read and Consensus Discussion in Breast Cancer Screening - A Retrospective Evaluation” | S2-SSBR01-3
Lunit INSIGHT MMG, “Comparing Different Scenarios for the Combined Use of Two Commercial AI Algorithms to Improve Mammography Interpretation and Decrease Radiologist Workload” | T7-SSBR07-5
Lunit INSIGHT DBT, “Use of Artificial Intelligence to Reduce the Interval Cancer Rate of Screening Digital Breast Tomosynthesis” | S5-SSBR02-2
Lunit INSIGHT DBT, “Performance of a Commercial Digital Breast Tomosynthesis Cancer Detection Model in a Large, Racially Diverse US Screening Population” | M1-SSBR04-5
Lunit INSIGHT CXR, “Real-World, Post-Market Surveillance Study of a Chest X-ray AI Model in a Multicenter Study” | T6-SSCH06-1
Lunit INSIGHT CXR, “Optimizing Chest Radiograph Workflow: Efficiency Gains of AI-Assisted Reporting in a Multi-Ethnic Cohort” | T6-SSCH06-4
Lunit INSIGHT CXR, “Does AI Assistance Improve Clinician Interpretation of Inpatient and Emergency Department Chest X-rays?” | W3-SSER02-6
Lunit INSIGHT CXR, “Role of AI for Chest X-ray Interpretation: is It a Valid Support?” | R5B-SPCH-4

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