Harnessing AI to tackle liver disease
Investigators in the Data Analytics and AI for Advanced Liver Disease Lab are committed to using cutting-edge digital tools, such as AI, to transform the continuum of care for liver disease.
Directed by co-principal investigators Vijay H. Shah, M.D., Patrick S. Kamath, M.D., Douglas A. Simonetto, M.D., and Alina M. Allen, M.D., the Data Analytics and AI for Advanced Liver Disease Laboratory seeks to develop artificial intelligence (AI) algorithms that can improve the care of patients with liver diseases.
Liver diseases are among the leading causes of global deaths and health-care costs. Significant unmet needs remain in the early detection of liver diseases and the prediction of outcomes. Patients with liver diseases generate an enormous volume of digital data from various sources including clinical documents, laboratory tests, radiologic and histopathologic images, genome sequencing, and continuous patient monitors. The abundance of information holds great promise to enhance the care of patients with liver diseases. But turning it into actionable knowledge has remained a major challenge.
Recently, revolutionary breakthroughs in the field of AI fueled by state-of-the-art machine learning algorithms, such as deep learning, have enabled computers to synthesize and analyze large volumes of complex, high-dimensional data with superhuman performances and speed. Digital transformation of health care using AI techniques has become one of the pillars of Mayo Clinic's 2030 strategy to transform patient and clinician experiences and solve humanity's most complex medical challenges.
In this light, our lab is actively working to develop novel AI-assisted tools that can enable early detection, accurate estimation of disease severity and prognosis, identification of novel risk factors, and automated interpretation of histopathology in patients with liver diseases. We are fortunate to collaborate with colleagues from a wide variety of disciplines within and outside of Mayo Clinic.
About Dr. Shah
Vijay Shah, M.D., serves as chair of the Department of Internal Medicine at Mayo Clinic's Minnesota campus in Rochester, Minnesota. He is a professor of medicine and physiology at Mayo Clinic College of Medicine and Science and is the Mayo Clinic-endowed Carol M. Gatton Professor of Digestive Diseases Research Honoring Peter Carryer, M.D.
Dr. Shah has maintained a National Institutes of Health (NIH)-funded program at Mayo Clinic for more than 20 years. This program focuses broadly on alcohol-related liver disease, cirrhosis, and portal hypertension and its complications. He has over 200 peer-reviewed publications in distinguished journals such as the Journal of Clinical Investigation, Nature, Proceedings of the National Academy of Sciences, New England Journal of Medicine and others. Dr. Shah is a member of the prestigious American Society for Clinical Investigation and the Association of American Physicians.
About Dr. Kamath
Patrick S. Kamath, M.D., a transplant hepatologist, is a professor of medicine for Mayo Clinic College of Medicine and Science. His research interests include investigating treatments for alcohol-associated hepatitis, cirrhosis and vascular diseases of the liver. He is committed to furthering the education of the next generation of physicians and has earned many prestigious educational awards.
About Dr. Simonetto
Douglas A. Simonetto, M.D., a gastroenterologist and certified transplant hepatologist, is an associate professor of Medicine for Mayo Clinic College of Medicine and Science. Dr. Simonetto's research interests are focused on developing artificial intelligence-enabled tools and digital solutions for application in patients with chronic liver disease.
About Dr. Allen
Alina M. Allen, M.D., is a hepatologist and an assistant professor of Medicine at Mayo Clinic College of Medicine and Science. Dr. Allen's research program focuses on improving patient outcomes in nonalcoholic fatty liver disease (NAFLD), which affects 80 million to 100 million people in the United States. Her current studies aim to use artificial intelligence to develop easily accessible methods for NAFLD screening and prediction of disease trajectory in this patient population.