Lunit plans to highlight seven AI-based studies at the upcoming RSNA meeting.
The studies include two oral presentations and five posters, and address AI advancements for chest x-ray reporting and breast cancer risk assessment, the company said.
Topics include the following:
- Lunit's AI model combines new and existing AI algorithms to filter normal chest radiographs from the radiology workload
- An exploration of the accuracy and robustness of Lunit's Insight MMG compared with radiologist readers
- Lunit's AI model tracks mammographic parenchymal patterns as predictive markers for breast cancer risk
- A comparison of the diagnostic accuracy of radiologists with and without the use of Lunit’s breast cancer screening algorithm
- An assessment of the value of supplemental breast ultrasound and Lunit’s AI software in screening mammography for women with dense breasts
- An assessment of the false-negative results of Lunit’s AI-based CADe/x in mammograms for invasive breast cancers
![A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/07/2026-07-14-radiology-mammogram-ai-auto-bias.H0bYO8QlWs.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
