LG’s Exaone Path 2.0 uses AI to transform genetic testing

LG AI Research has introduced Exaone Path 2.0, an upgraded AI model designed to analyse pathology images for disease diagnosis, significantly reducing the time required for genetic testing.

The new model, unveiled Wednesday, can reportedly process pathology images in under a minute—a significant shift from conventional genetic testing methods that often take more than two weeks.

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.

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.

The model has also been tailored for specific applications in oncology, including lung and colorectal cancers, where it can help clinicians identify patient groups most likely to benefit from targeted therapies.

LG AI Research is collaborating with Professor Hwang Tae-hyun and his team at Vanderbilt University Medical Centre in the US to further its application in real-world clinical settings.

Their shared goal is to develop a multimodal medical AI platform that can support precision medicine directly within clinical environments.

Hwang, a key contributor to the US government’s Cancer Moonshot program and founder of the Molecular AI Initiative at Vanderbilt, emphasised that the aim is to create AI tools usable by clinicians in active medical practice, rather than limiting innovation to the lab.

In addition to oncology, LG AI Research plans to extend its multimodal AI initiatives into transplant rejection, immunology, and diabetes.

It is also collaborating with the Jackson Laboratory to support Alzheimer’s research and working with Professor Baek Min-kyung’s team at Seoul National University on next-generation protein structure prediction.

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