{"id":275766,"date":"2025-07-01T07:17:01","date_gmt":"2025-07-01T12:17:01","guid":{"rendered":"https:\/\/dallasinnovates.com\/?p=275766"},"modified":"2025-06-27T17:32:14","modified_gmt":"2025-06-27T22:32:14","slug":"ut-dallas-study-tests-ai-as-a-triage-tool-in-breast-cancer-screenings","status":"publish","type":"post","link":"https:\/\/dallasinnovates.com\/ut-dallas-study-tests-ai-as-a-triage-tool-in-breast-cancer-screenings\/","title":{"rendered":"UT Dallas Study Tests AI as a Triage Tool in Breast Cancer Screenings"},"content":{"rendered":"<p data-start=\"403\" data-end=\"625\"><strong>Artificial intelligence can be a useful tool in identifying high-risk breast cancer cases, allowing for quicker reviews by radiologists, according to two information systems professors at the University of Texas at Dallas.<\/strong><\/p>\n<p data-start=\"627\" data-end=\"1025\">That\u2019s important, UT Dallas said, because breast cancer rates, particularly among younger women, have been rising since 2012 while the number of radiologists in the U.S. has continued to decline. Professors Mehmet Ayvaci and Radha Mookerjee, along with colleagues from other institutions, found that AI could reduce healthcare costs by up to 30 percent when compared to relying solely on human diagnosis.<\/p>\n<p data-start=\"1027\" data-end=\"1161\">However, Ayvaci said, removing humans from the diagnostic process entirely isn&#8217;t advisable because AI is still not as accurate as a radiologist.<\/p>\n<p>\u201cThe workflow and most cost-effective strategy are the question,\u201d Ayvaci said. \u201cI\u2019m talking about how to replace a task, not replace decision-making. Could we design a workflow where AI is playing a triage role?\u201d<\/p>\n<p data-start=\"1377\" data-end=\"1514\">Both researchers are faculty members in the Naveen Jindal School of Management. Their findings were published in <a href=\"https:\/\/www.nature.com\/articles\/s41467-025-57409-1\">Nature Communications.<\/a><\/p>\n<h3>Studying mammography images with AI<\/h3>\n<p data-start=\"1557\" data-end=\"1915\">The team built its models using real mammography images from the Digital Mammography DREAM Challenge, a publicly available dataset of more than 640,000 anonymous images. Gustavo Stolovitzky, a co-author of the study, is founder and chair emeritus of the DREAM Challenges, a crowdsourced initiative that applies collaborative data science to medical research.<\/p>\n<p data-start=\"1917\" data-end=\"2422\">\u201cOur approach enabled us to precisely identify which patients could safely be evaluated using AI alone while determining which ones would need to be referred to a human,\u201d Mookerjee said in a statement. <\/p>\n<p>\u201cEvery patient\u2019s data, including the mammography images, is input to an AI algorithm, which outputs a measure of risk,&#8221; he added. &#8220;You can think of this measure as a probability that the patient has breast cancer.&#8221;<\/p>\n<p>That risk value is used to determine whether the patient should be referred to a human, Mookerjee says.<\/p>\n<p data-start=\"2424\" data-end=\"2640\">According to UT Dallas, integrating AI into mammogram screenings could help reduce false positives, which often lead to unnecessary procedures such as biopsies. A false negative from AI, however, raises important concerns.<\/p>\n<p>\u201cIf AI makes a wrong decision, who should be held accountable? All of these are open questions,\u201d Ayvaci said.<\/p>\n<h3>&#8216;Brighter future&#8217; of AI systems<\/h3>\n<p data-start=\"2797\" data-end=\"3059\">Ayvaci said AI\u2019s effectiveness in diagnostic settings hasn\u2019t been fully tested in real-world healthcare environments. He is also researching how AI could reduce administrative burdens and improve cost efficiency, especially in rural clinics or underserved areas.<\/p>\n<p>\u201cThe new generation of AI systems are capable of providing context and improving in a much faster timeline,\u201d Ayvaci said. \u201cAll of this contributes to a brighter future of using these AI systems.\u201d<\/p>\n<p data-start=\"3254\" data-end=\"3397\">Mookerjee, whose mother is a breast cancer survivor, said she was drawn to the project by the opportunity to apply AI to healthcare challenges.<\/p>\n<p>\u201cWhen Dr. Ayvaci told me about his idea to use AI in screening mammography images, it seemed like the perfect merger of these two areas,\u201d she said.<\/p>\n<p data-start=\"3548\" data-end=\"3777\">Ayvaci and co-author Mehmet Eren Ahsen of the University of Illinois Urbana-Champaign are now exploring how this AI approach could help speed up patient care by supporting same-day procedures or faster follow-ups after diagnosis.<\/p>\n<h3 data-start=\"3548\" data-end=\"3777\">Read next<\/h3>\n<p class=\"p2\">The UT Dallas study adds to a growing body of AI-driven breast cancer research in North Texas. Last year, we told you about how <a href=\"https:\/\/dallasinnovates.com\/utsw-team-develops-new-ai-tool-to-detect-possible-metastatic-breast-cancer\/\">a novel artificial intelligence model<\/a> to improve the detection of breast cancer metastasis has been developed by researchers at UT Southwestern Medical Center. <\/p>\n<p>UTSW said the model could reduce the need for needle or surgical biopsies.<\/p>\n<hr style=\"border: none; height: 4px; background: #e5e5e5; margin: 20px 0; width: 100%;\" \/>\n<h2 style=\"font-size: 1.5rem; font-weight: 600; margin: 0 0 16px 0;\">Don&#8217;t miss what&#8217;s next. Subscribe\u00a0to\u00a0Dallas\u00a0Innovates.<\/h2>\n<p style=\"font-size: 1rem; line-height: 1.5; margin: 0 0 10px 0;\">Track Dallas-Fort Worth&#8217;s business and innovation landscape with our curated news in your inbox Tuesday-Thursday.<\/p>\n<div id=\"mc_embed_signup\">\n<form id=\"mc-embedded-subscribe-form\" class=\"validate\" style=\"display: flex; flex-wrap: wrap; gap: 12px; align-items: center;\" action=\"https:\/\/dallasinnovates.us10.list-manage.com\/subscribe\/post?\" method=\"post\" name=\"mc-embedded-subscribe-\nform\" novalidate=\"\" target=\"_blank\"><\/p>\n<div id=\"mc_embed_signup_scroll\" style=\"display: flex; flex-wrap: wrap; gap: 12px; align-items: center;\">One quick signup, and you&#8217;re done.<\/p>\n<div style=\"display: flex; gap: 8px;\"><input id=\"mce-EMAIL\" class=\"email\" style=\"width: 300px; padding: 8px 12px; border: 1px solid #d1d5db; border-radius: 4px; font-size: 1rem;\" name=\"EMAIL\" required=\"\" type=\"email\" value=\"\" placeholder=\"email address\" \/> <input id=\"mc-embedded-subscribe\" class=\"button\" style=\"background-color: #4b0082; color: white; padding: 8px 20px; border: none; border-radius: 4px; cursor: pointer; font-weight: 500; transition: background-color 0.2s;\" name=\"subscribe\" type=\"submit\" value=\"Subscribe\" \/><\/div>\n<\/div>\n<\/form>\n<\/div>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Rather than replace radiologists, UTD researchers say AI could help flag low-risk mammograms for automated review. The approach could cut costs and ease delays as cancer rates rise and the diagnostic workforce shrinks.<\/p>\n","protected":false},"author":129,"featured_media":276313,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[201981,9,12],"tags":[172483,204815,3685,7859,204812,122593,191597,3877,104,204814,204734,8808,204813,204732,32744,174,168177,204733,204540,53,243,52],"class_list":["post-275766","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-discovery","category-education","category-invention","tag-ai-in-healthcare","tag-ai-workflow","tag-artificial-intelligence","tag-breast-cancer","tag-breast-cancer-detection","tag-costs","tag-diagnostic-imaging","tag-digital-health","tag-healthcare","tag-healthcare-cost-savings","tag-information-systems-associate-professors","tag-machine-learning","tag-mammography","tag-mehmet-ayvaci","tag-nature-communications","tag-naveen-jindal-school-of-management","tag-north-texas-research","tag-radha-mookerjee","tag-radiologists","tag-university-of-texas-at-dallas","tag-ut-dallas","tag-utd"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v26.6 (Yoast SEO v26.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>UT Dallas Study Tests AI as a Triage Tool in Breast Cancer Screenings &#187; 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