Lunit, Inc.

According to a study published in the European Journal of Cancer , the fairness and equity of datasets for AI-driven mammogram interpretation might be jeopardized by the underrepresentation of racial and ethnic diversity. While AI shows promise for enhancing how mammograms are interpreted, particularly in areas where resources are limited, the study's authors found warning signs regarding the diversity of datasets and the representation of researchers in AI model development, which they said could "affect the models’ generalizability, fairness and equity." For the study, researchers conducted a scientometric review of studies published in 2017, 2018, 2022 and 2023 utilizing screening or diagnostic mammograms for breast cancer detection to "train or validate AI algorithms." Of the 5,774 studies identified, 264 met the inclusion criteria. "The number of studies increased from 28 in 2017 to 2018 to 115 in 2022 to 2023 – a 311% increase. Despite this growth, only 0-25% of studies reported race/ethnicity, with most patients identified as Caucasian," the study's authors wrote. "Moreover, nearly all patient cohorts originated from high-income countries, with no studies from low-income settings. Author affiliations were predominantly from high-income regions and gender imbalance was observed among first and last authors." The authors concluded: "The lack of racial, ethnic and geographic diversity in both datasets and researcher representation could undermine the generalizability and fairness of AI-based mammogram interpretation." Furthermore, recognizing the disparities through diverse dataset collection and comprehensive international collaborations is crucial to guaranteeing fair advancements in breast cancer care. Study data revealed that algorithms focusing mostly on Caucasian populations could result in inaccurate outcomes and the wrong diagnosis in underrepresented populations. Additionally, patient outcomes may be threatened and current disparities could worsen. "The fairness of these AI tools is called into question, as they risk systematically dis-advantaging certain racial, ethnic or socio-demographic groups. To mitigate these issues and ensure that the benefits of AI in BC imaging are equitably distributed, it is essential to prioritize diversity in dataset collection, foster international collaborations that include researchers from lower and middle-income countries and actively incorporate diverse populations in clinical research," the study's authors wrote. THE LARGER TREND In February, Google partnered with the Institute of Women's Cancers , founded by France's cancer research and treatment center Institut Curie, to study how AI tools can help address cancer, share science-based health information and support postdoctoral researchers with funding. The two entities looked into how AI-based tools can help forecast the progression of cancer and the likelihood of relapse for patients, with the goal of developing more accurate and successful treatments. The researchers focused on hard to treat women's cancers, including triple-negative breast cancer, an aggressive type of breast cancer that grows and spreads faster than other types. In 2024, AI biotech company Owkin partnered with pharma giant AstraZeneca to develop an AI-powered tool designed to pre-screen for gBRCA mutations (gBRCAm) in breast cancer directly from digitized pathology slides. The aim of the tool is to speed up and increase access to gBRCA testing that some patients may not be considered for. That same year, Lunit , a provider of AI-powered solutions for cancer diagnostics and therapeutics, and Volpara Health , a company offering AI-powered software to help providers better understand cancer risk, joined forces to develop a comprehensive ecosystem for early cancer detection, cancer risk prediction and independent AI to improve clinical workflows. In May of that year, Lunit acquired Volpara and integrated its AI breast health platforms, including its Scorecard breast density assessment tool, into its line of AI tools for breast cancer detection. Before it acquired Volpara, Lunit partnered with one of the country's biggest private healthcare providers to help raise Sweden's cancer screening capability . In 2023, Lunit signed a three-year agreement with Capio S:t Göran Hospital to supply and license its AI-powered mammography analysis software Lunit INSIGHT MMG. The AI tool enabled the hospital to analyze breast images of approximately 78,000 patients yearly.

According to a study published in the European Journal of Cancer, the fairness and equity of datasets for AI-driven mammogram interpretation might be jeopardized by the underrepresentation of racial and ethnic diversity. While AI shows promise for enhancing how mammograms are interpreted, particularly in areas where resources are limited, the study's authors found warning signs regarding the diversity of datasets and the representation of researchers in AI model development, which they said could "affect the models’ generalizability, fairness and equity." For the study, researchers conducted a scientometric review of studies published in 2017, 2018, 2022 and 2023 utilizing screening or diagnostic mammograms for breast cancer detection to "train or validate AI algorithms." Of the 5,774 studies identified, 264 met the inclusion criteria. "The number of studies increased from 28 in 2017 to 2018 to 115 in 2022 to 2023 – a 311% increase. Despite this growth, only 0-25% of studies reported race/ethnicity, with most patients identified as Caucasian," the study's authors wrote. "Moreover, nearly all patient cohorts originated from high-income countries, with no studies from low-income settings. Author affiliations were predominantly from high-income regions and gender imbalance was observed among first and last authors." The authors concluded: "The lack of racial, ethnic and geographic diversity in both datasets and researcher representation could undermine the generalizability and fairness of AI-based mammogram interpretation." Furthermore, recognizing the disparities through diverse dataset collection and comprehensive international collaborations is crucial to guaranteeing fair advancements in breast cancer care. Study data revealed that algorithms focusing mostly on Caucasian populations could result in inaccurate outcomes and the wrong diagnosis in underrepresented populations. Additionally, patient outcomes may be threatened and current disparities could worsen. "The fairness of these AI tools is called into question, as they risk systematically dis-advantaging certain racial, ethnic or socio-demographic groups. To mitigate these issues and ensure that the benefits of AI in BC imaging are equitably distributed, it is essential to prioritize diversity in dataset collection, foster international collaborations that include researchers from lower and middle-income countries and actively incorporate diverse populations in clinical research," the study's authors wrote. THE LARGER TREND In February, Google partnered with the Institute of Women's Cancers, founded by France's cancer research and treatment center Institut Curie, to study how AI tools can help address cancer, share science-based health information and support postdoctoral researchers with funding. The two entities looked into how AI-based tools can help forecast the progression of cancer and the likelihood of relapse for patients, with the goal of developing more accurate and successful treatments. The researchers focused on hard to treat women's cancers, including triple-negative breast cancer, an aggressive type of breast cancer that grows and spreads faster than other types. In 2024, AI biotech company Owkin partnered with pharma giant AstraZeneca to develop an AI-powered tool designed to pre-screen for gBRCA mutations (gBRCAm) in breast cancer directly from digitized pathology slides. The aim of the tool is to speed up and increase access to gBRCA testing that some patients may not be considered for. That same year, Lunit, a provider of AI-powered solutions for cancer diagnostics and therapeutics, and Volpara Health, a company offering AI-powered software to help providers better understand cancer risk, joined forces to develop a comprehensive ecosystem for early cancer detection, cancer risk prediction and independent AI to improve clinical workflows. In May of that year, Lunit acquired Volpara and integrated its AI breast health platforms, including its Scorecard breast density assessment tool, into its line of AI tools for breast cancer detection. Before it acquired Volpara, Lunit partnered with one of the country's biggest private healthcare providers to help raise Sweden's cancer screening capability. In 2023, Lunit signed a three-year agreement with Capio S:t Göran Hospital to supply and license its AI-powered mammography analysis software Lunit INSIGHT MMG. The AI tool enabled the hospital to analyze breast images of approximately 78,000 patients yearly.

Despite strong performance from AI, fewer recalls from AI-flagged cases point to challenges in human-AI collaboration SEOUL, South Korea, April 9, 2025 /PRNewswire/ -- Lunit (KRX:328130.KQ), a leading provider of AI-powered solutions for cancer diagnostics and therapeutics, today announced the publication of a new study in Radiology (RSNA's flagship journal), highlighting how radiologists interact with AI in real-world breast cancer screening. Continue Reading Lunit’s AI identified more cancers, but radiologists recalled fewer of those cases—highlighting a trust gap in clinical decision-making. The study, based on the prospective ScreenTrustCAD 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 Göran Hospital in Stockholm, 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 AI—even though those cases were more likely to be cancerous—pointing to a trust gap between human clinicians and AI tools. "This study isn't just about how well the AI performs—it's about what happens when AI enters the decision-making process," said Brandon Suh, CEO of Lunit. "The findings point to a gap between AI input and human response, and that's 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 recalled—but 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 cases—raising questions about how AI findings are weighed in clinical judgment. "This isn't a question of whether AI can detect cancer. It's 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 at Karolinska Institutet. Clinical Performance of Lunit INSIGHT MMG While the study's 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 AI's ability to detect cancers that may go unnoticed in standard double-reading workflows—particularly 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 radiologists—indicating 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." Access the full study published in Radiology here. About Lunit Founded in 2013, Lunit (KRX:328130.KQ) is a medical AI company on a mission to conquer cancer through AI. Lunit harnesses AI-powered medical image analytics and biomarker analysis to ensure accurate diagnosis and optimal treatment for each cancer patient. The FDA-cleared Lunit INSIGHT suite for cancer screening serves over 4,800 medical institutions across 55+ countries. Lunit clinical studies have been published in top journals, including the Journal of Clinical Oncology and the Lancet Digital Health, and presented at global conferences such as the ASCO and RSNA. Headquartered in Seoul, South Korea, with a network of offices worldwide, Lunit leads the global fight against cancer. Discover more at lunit.io. SOURCE Lunit WANT YOUR COMPANY'S NEWS FEATURED ON PRNEWSWIRE.COM? 440k+ Newsrooms & Influencers 9k+ Digital Media Outlets 270k+ Journalists Opted In GET STARTED