LG is partnering with Vanderbilt University to bring AI models like Exaone Path 2.0 into real-world clinical settings.<\/p>\n
LG AI Research has introduced Exaone Path 2.0<\/a>, an upgraded AI model designed to analyse pathology images for disease diagnosis, significantly reducing the time required for genetic testing.<\/p>\n The new model, unveiled Wednesday, can reportedly process pathology images in under a minute\u2014a significant shift from conventional genetic testing methods that often take more than two weeks.<\/p>\n According to LG, the AI system offers enhanced accuracy in detecting genetic mutations and gene expression patterns by learning from detailed image patches and full-slide pathology data.<\/p>\n Developed by LG AI Research, a division of the LG Group, Exaone Path 2.0 is trained on over 10,000 whole-slide images (WSIs) and multiomics pairs, enabling it to integrate structural information with molecular biology insights. The company said it has achieved a 78.4 percent accuracy rate in predicting genetic mutations.<\/p>\n The model has also been tailored for specific applications in oncology, including lung and colorectal cancers<\/a>, where it can help clinicians identify patient groups most likely to benefit from targeted therapies.<\/p>\n LG AI Research is collaborating with Professor Hwang Tae-hyun and his team at Vanderbilt University Medical Centre in the US<\/a> to further its application in real-world clinical settings.<\/p>\n Hwang, a key contributor to the US government\u2019s Cancer Moonshot program and founder of the Molecular AI Initiative at Vanderbilt, emphasised that the aim is to create AI tools <\/a>usable by clinicians in active medical practice, rather than limiting innovation to the lab.<\/p>\n In addition to oncology, LG AI Research plans to extend its multimodal AI initiatives into transplant rejection, immunology, and diabetes.<\/p>\n It is also collaborating with the Jackson Laboratory to support Alzheimer\u2019s research and working with Professor Baek Min-kyung\u2019s team at Seoul National University on next-generation protein structure prediction.<\/p>\n