KAIST announced on the 14th of November that it has been selected as a major participating institution in the 'Lunit Consortium' for the 'AI Specialized Foundation Model Development Project' supervised by the Ministry of Science and ICT, and has officially started developing an AI foundation model for the medical science and bio fields. Through this project, KAIST plans to develop an 'AI Foundation Model Specialized for Medical Science' that encompasses the entire lifecycle of bio and medical data, and lead the creation of an AI based life science innovation ecosystem. The 'Lunit Consortium' includes 7 companies-Lunit, Trillion Labs, Kakao Healthcare, Igenscience, SK Biopharm, and Rebellion-along with 9 medical and research institutions, including KAIST, Seoul National University, NYU, National Health Insurance Service Ilsan Hospital, and Yonsei Severance Hospital. This consortium will be supported by 256 state of the art B200 GPUs to build and demonstrate a 'Chain of Evidence-Based Full-Cycle Medical Science AI Model', an AI system that connects and analyzes medical data from beginning to end, and a 'Multi-Agent Service', a system where multiple AIs collaborate to perform diagnosis and prediction. KAIST's participation in this project involves a joint research team formed by professors from the School of Computing and the Kim Jaechul Graduate School of AI. Professors Yoonjae Choi, Tae-Kyun Kim, Jong Chul Ye, Hyunwoo Kim, and Seunghoon Hong will serve as the research team, and Vice President for Research Sang Yup Lee will take on an advisory role. The research team is not merely collecting data but they are establishing a strategy (L1~L7 stages) to precisely process and systematically manage medical and life science data so that the AI can actually learn and utilize it. Through this, they plan to develop and verify an AI model that connects and analyzes diverse life science data, including medical information, gene/protein data, and new drug candidates. The data the research team aims to integrate includes a wide range from language to actual patient treatment information. Specifically, L1 represents language data, L2 is the structure of molecules, L3 is proteins and antibodies, L4 is omics data encompassing genetic and protein information, L5 is drug information, L6 is medical science research and clinical data, and L7 is real-world clinical data obtained from actual hospitals. In essence, the data handled by the AI connects everything from speech and text to molecules, proteins, drugs, clinical research, and actual patient treatment information.

Vice President Sang Yup Lee is a world-renowned scholar in the fields of synthetic biology and systems metabolic engineering, leading the establishment of a bio manufacturing platform and policy advice through the convergence of life science, engineering, and AI. He advises on the analysis of life information (omics) such as genes and proteins and designs a feedback system for verifying experimental results, supporting the Korean-developed medical AI model to secure international reliability and competitiveness. Vice President Lee stated, "AI technology is breaking down the boundaries of life science and engineering, creating a new paradigm for knowledge creation," adding, "KAIST will utilize full cycle medical science data to accelerate the era where AI uncovers the causes of diseases and predicts treatments." KAIST President Kwang Hyung Lee said, "KAIST will contribute to creating an AI-based life science innovation ecosystem, lead the innovation of national strategic industries through world-class AI-bio convergence research, and drive the progress of human health and science and technology." The model developed in the Lunit Consortium will be released as an Open License for commercial use, and is expected to expand into various medical and healthcare services such as national health chatbots. With this participation, KAIST plans to strengthen research on AI-based life science data infrastructure establishment, medical AI standardization, and AI ethics and policy advice, leading the AI transition of national bio and medical science research.