Cerebras Systems, a pioneer in accelerating generative AI, today announced a collaboration with Mayo Clinic as its first generative AI collaborator for the development of large language models (LLMs) for medical applications. Unveiled at the JP Morgan Healthcare Conference in San Francisco, the multi-year collaboration is already producing pioneering new LLMs to improve patient outcomes and diagnoses, leveraging Mayo Clinic’s robust longitudinal data repository and Cerebras’ industry-leading generative AI compute to accelerate breakthrough insights.
Healthcare is currently 17% of GDP in the United States, making it not only one of the country’s largest economic sectors, but also the industry with the greatest potential to transform the human experience and improve the lives of its citizens. To create the first truly patient-centric healthcare AI, Mayo Clinic selected Cerebras for its proven experience in designing and training large scale, domain-specific generative AI models.
“It is an honor to collaborate with Mayo Clinic, the top-ranked hospital in the nation. With its recognized leadership in delivering medical outcomes, we are uniquely positioned to combine AI and medicine,” said Andrew Feldman, CEO and co-founder, Cerebras. “The state-of-the-art AI models we are developing together will work alongside doctors to help with patient diagnosis, treatment planning, and outcome estimation.”
A first deliverable of the collaboration is a Rheumatoid Arthritis (RA) diagnostic model, which will combine data from patient records, DNA and drug molecules to help match RA patients with the best therapeutics to manage their disease. Mayo Clinic and Cerebras seek to develop a similar model for other disease states.
Training large AI models requires a massive amount of compute, vast datasets, and specialized AI expertise. The Cerebras CS-2, powered by the WSE-2, is purpose-built for generative AI and delivers a rare combination of world-leading AI compute, software and AI expertise. Cerebras has a proven record of developing extremely high-quality, domain specific generative AI models in record time, as well as remarkable scientific results, often characterized as “100x” improvements in medical and scientific computing.