Dec. 2, 2025 — Lunit, a provider of AI for cancer diagnostics and precision oncology, will present 14 studies at RSNA 2025. Spanning screening mammography, digital breast tomosynthesis (DBT), breast density science and risk modeling, this year’s program represents one of the company’s most extensive evidence portfolios to date. Lunit will exhibit at South Hall, Booth #4100, and host expert-led sessions in the Lunit Education Room #1252.
Among the research, real-world results from Capio S:t Göran Hospital in Sweden evaluated more than 193,000 screening examinations before and after the introduction of Lunit INSIGHT MMG. When AI was paired with a single radiologist, the screening program achieved higher invasive cancer detection, greater positive predictive value and fewer unnecessary recalls compared to human-only double reading — reinforcing findings from the ScreenTrustCAD interventional trial and demonstrating AI-integrated single reading can maintain or improve accuracy while alleviating workforce pressure.
New evidence from Massachusetts General Hospital assessed AI performance on screening DBT using 1,000 retrospective examinations, showing that Lunit INSIGHT DBT correctly localized 84.4% of true-positive cancers, with particularly strong performance for mass-presenting and invasive ductal carcinoma cases. The study also detailed cancer subtypes less likely to be identified by AI, offering practical insight into the algorithm’s strengths and limitations as DBT adoption accelerates worldwide.
Further expanding the evidence base, two oral presentations from Elizabeth Wende Breast Care explored how volumetric breast density, as quantified by AI-powered algorithms, and family history influence risk stratification across leading models, as well as how density changes over time affect prediction consistency. In a cohort of 44,651 women, Tyrer-Cuzick classified substantially more women as high-risk than BOADICEA, driven by heavier weighting of density and family history inputs. A complementary longitudinal analysis of 335,000+ images demonstrated that incorporating volumetric breast density over time improved calibration and discrimination, while static or categorical density inputs consistently underestimated cancer incidence. Together, these findings strengthen the role of density-informed and longitudinal risk modeling in supporting risk-adapted screening strategies.
Lunit will also host a series of expert-led sessions in its Education Room #1252, offering deeper clinical and scientific perspectives.Highlights include:
- Image-Based Risk (Nov 30, 1:30 PM): A cross-disciplinary discussion featuring Drs. Graham Colditz, Joy Jiang, and Hari Trivedi, exploring how image-based risk complements traditional models and what this means for more personalized, risk-adapted screening strategies.
- Interval Cancer Detection with AI (Dec 1, 11:30 AM): Prof. Fiona Gilbert (University of Cambridge) will present new evidence on the ability of AI to identify interval breast cancers typically missed during routine screening, outlining how AI-supported detection could reduce missed cancers and improve population-level screening outcomes.
- Academic Leadership in AI Adoption (Dec 2, 10:30– AM): Drs. Liz Morris (UC Davis Health) and Elizabeth Burnside (University of Wisconsin–Madison) will compare two distinct approaches to implementing breast AI in academic environments. The session will examine evidence, ethics, and practical considerations for responsible adoption.
- Real-World DBT Performance (Dec 2, 4:30 PM): Dr. Manisha Bahl (Mass General) will share new clinical data on how Lunit INSIGHT DBT performs in digital breast tomosynthesis workflows, highlighting improvements in cancer detection, diagnostic consistency, and the potential to reduce interval cancer rates when integrated into routine practice.
“RSNA is where the field looks to understand whether AI is delivering measurable clinical value, and this year’s program reflects that momentum,” said Brandon Suh, CEO of Lunit. “Across mammography, DBT, and density-driven risk modeling, our studies highlight how rigorously validated AI can enhance screening performance, support radiologists, and strengthen the foundation for risk-adapted care. We remain committed to generating evidence that drives responsible and meaningful adoption in clinical practice.”
Learn more at lunit.io/en.
February 06, 2026 
