{"id":201695,"date":"2024-12-15T19:27:08","date_gmt":"2024-12-16T01:27:08","guid":{"rendered":"https:\/\/lifeboat.com\/blog\/2024\/12\/ai-medical-imagery-model-offers-fast-cost-efficient-expert-analysis"},"modified":"2024-12-15T19:27:08","modified_gmt":"2024-12-16T01:27:08","slug":"ai-medical-imagery-model-offers-fast-cost-efficient-expert-analysis","status":"publish","type":"post","link":"https:\/\/lifeboat.com\/blog\/2024\/12\/ai-medical-imagery-model-offers-fast-cost-efficient-expert-analysis","title":{"rendered":"AI Medical Imagery Model Offers Fast, Cost-Efficient Expert Analysis"},"content":{"rendered":"<p><a class=\"aligncenter blog-photo\" href=\"https:\/\/lifeboat.com\/blog.images\/ai-medical-imagery-model-offers-fast-cost-efficient-expert-analysis2.jpg\"><\/a><\/p>\n<p>Researchers at UCLA have developed a new AI model that can expertly analyze 3D medical images of diseases in a fraction of the time it would otherwise take a human clinical specialist.<\/p>\n<p>The deep-learning framework, named SLIViT (<strong>SL<\/strong>ice <strong>I<\/strong>ntegration by <strong>Vi<\/strong>sion <strong>T<\/strong>ransformer), analyzes images from different imagery modalities, including retinal scans, ultrasound videos, CTs, MRIs, and others, identifying potential disease-risk biomarkers.<\/p>\n<p>Dr. Eran Halperin, a computational medicine expert and professor at UCLA who led the study, said the model is highly accurate across a wide variety of diseases, outperforming many existing, disease-specific foundation models. It uses a novel pre-training and fine-tuning method that relies on large, accessible public data sets. As a result, Halperin believes that the model can be deployed\u2014at relatively low costs\u2014to identify different disease biomarkers, democratizing expert-level medical imaging analysis.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Researchers at UCLA have developed a new AI model that can expertly analyze 3D medical images of diseases in a fraction of the time it would otherwise take a human clinical specialist. The deep-learning framework, named SLIViT (SLice Integration by Vision Transformer), analyzes images from different imagery modalities, including retinal scans, ultrasound videos, CTs, MRIs, [\u2026]<\/p>\n","protected":false},"author":662,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11,6],"tags":[],"class_list":["post-201695","post","type-post","status-publish","format-standard","hentry","category-biotech-medical","category-robotics-ai"],"_links":{"self":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/201695","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/users\/662"}],"replies":[{"embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/comments?post=201695"}],"version-history":[{"count":0,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/posts\/201695\/revisions"}],"wp:attachment":[{"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/media?parent=201695"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/categories?post=201695"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/lifeboat.com\/blog\/wp-json\/wp\/v2\/tags?post=201695"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}