MELD Health is proud to announce the addition of Dr. Kayvan Najarian as a key strategic advisor, bringing his world-renowned expertise in biomedical informatics, data science, and artificial intelligence to guide the evolution of MELD Health’s proprietary AI-driven clinical tools. With more than 350 peer-reviewed publications and over 40 patents, several of which have been successfully commercialized through both startups and global industry partners, Dr. Najarian is widely recognized as a pioneer in applying computational intelligence to healthcare. He is also the author of two leading textbooks in biomedical informatics, which are used in universities and research institutions around the world.
Dr. Najarian holds multiple leadership positions at the University of Michigan, where he serves as a Professor in the Gilbert S. Omenn Department of Computational Medicine and Bioinformatics (DCMB), as well as in the departments of Emergency Medicine and Electrical Engineering and Computer Science. He also directs the Biomedical and Clinical Informatics Laboratory (BCIL) and serves as Associate Director of the Weil Institute for Critical Care Research and Innovation. Most notably, as Director of the Michigan Institute for Data and AI in Society (MIDAS), Dr. Najarian leads interdisciplinary collaborations focused on data science applications across biological and health sciences. His research in computer-assisted decision-making systems has advanced the intersection of AI and clinical care, aligning directly with MELD Health’s mission to transform the management of chronic pain.
“We are deeply honored to have Dr. Najarian join our advisory team,” said David Hanna, CEO of MELD Health. “His unparalleled experience in clinical AI, coupled with his leadership in translating data science into real-world healthcare solutions, will be instrumental in helping MELD Health shape the next generation of intelligent tools for pain management. Dr. Najarian’s guidance will ensure that our solutions not only keep pace with the rapid evolution of large language models but also remain purpose-built to close gaps in clinical efficiency and patient engagement—delivering truly state-of-the-art support for clinicians and patients alike.”
