Douglas A. Simonetto, MD: Using Artificial Intelligence in Hepatology Trials

April 27, 2022
Kenny Walter

Kenny Walter is an editor with HCPLive. Prior to joining MJH Life Sciences in 2019, he worked as a digital reporter covering nanotechnology, life sciences, material science and more with R&D Magazine. He graduated with a degree in journalism from Temple University in 2008 and began his career as a local reporter for a chain of weekly newspapers based on the Jersey shore. When not working, he enjoys going to the beach and enjoying the shore in the summer and watching North Carolina Tar Heel basketball in the winter.

Dr. Simonetto is involved in several studies using artificial intelligence.

The advent and utilization of new technologies like artificial intelligence (AI) could help boost clinical trials and more efficiently develop new drugs for a wide array of diseases and conditions.

One such area where this is being considered is hepatology, specifically with alcohol-associated liver disease (AALD).

Douglas A. Simonetto, Assistant Professor of Gastroenterology and Hepatology, Mayo Clinic, is focusing new research using AI in detecting disease in patients.

Simonetto explained in an interview with HCPLive® that 1 of the goals is to try to find ways to detect alcohol-associated liver disease and alcohol use disorder in the general population so that interventions can be more effective.

The technology is being used to try to identify risk factors in patients that may not otherwise have been identified by a clinician.

They are also exploring traditional tools like electrocardiograms to capture signals with an AI algorithm for various liver diseases.

Another application of AI is trying to identify additional phenotypes in patients with liver disease and alcohol use disorder. Simonetto said a team is collecting biodata using smart phone applications, while also gathering survey information from patients on cravings.

In the future, Simonetto said he would like to see the platform be used more on the treatment side than the data collection side.