Lunit Study in Radiology Highlights Trust Gap Between Radiologists and AI in Breast Cancer Screening
Despite strong performance from AI, fewer recalls from AI-flagged cases point to challenges in human-AI collaboration [Press Release] SEOUL, South Korea,April 9, 2025-- Lunit(루닛), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the publication of a new study inRadiology(RSNAs flagship journal), highlighting how radiologists interact with AI in real-world breast cancer screening. Lunits AI identified more cancers, but radiologists recalled fewer of those caseshighlighting a trust gap in clinical decision-making. The study, based on the prospectiveScreenTrustCAD trial, is the first large-scale, population-based trial to evaluate how radiologists interact with AI in an operational breast cancer screening setting. Conducted at Capio St Gran Hospital inStockholm, Sweden, the trial enrolled about 55,000 women and implemented Lunit INSIGHT MMG as an independent third reader alongside two radiologists. The findings suggest that radiologists were significantly less likely to recall patients flagged by AIeven though those cases were more likely to be cancerouspointing to a trust gap between human clinicians and AI tools. This study isnt just about how well the AI performsits about what happens when AI enters the decision-making process, saidBrandon Suh, CEO of Lunit. The findings point to a gap between AI input and human response, and thats where real-world impact is made or lost. Human-AI Collaboration The study showed that: -When only AI flagged a case, just 4.6% were recalled by radiologists. -When only a radiologist flagged a case, 14.2% were recalled. -When two radiologists flagged a case, 57.2% were recalledbut when AI and one radiologist both flagged it, the recall rate dropped to 38.6%. Despite being blinded to AI scores during the initial review, radiologists consistently recalled fewer patients from AI-flagged casesraising questions about how AI findings are weighed in clinical judgment. This isnt a question of whether AI can detect cancer. Its about how AI findings are interpreted and acted on by the people making clinical decisions, said Dr. Karin Dembrower, lead author of the study and radiologist atKarolinska Institutet. Clinical Performance of Lunit INSIGHT MMG While the studys central focus was on decision-making dynamics, it also confirmed the strong diagnostic performance of Lunit INSIGHT MMG: -Among women recalled after screening, 22% of AI-only flagged cases were diagnosed with cancer. -In contrast, just 3.4% of single-radiologist flags and 2.5% of two-radiologist flags resulted in a cancer diagnosis. -When both AI and one radiologist flagged a case, the cancer yield was highest at 25%. These results highlight AIs ability to detect cancers that may go unnoticed in standard double-reading workflowsparticularly in early or less obvious presentations. These findings suggest that while the AI system correctly identified high-risk cases, many of those cases were not recalled by radiologistsindicating a potential gap in trust or confidence in AI-generated findings. The AI clearly shows strong diagnostic performance, Suh added. What this study reveals is that realizing that potential in daily clinical practice depends on how AI and humans work together. News provided by Lunit