Machine learning may help identify biomarkers and develop predictive models that could assist in the diagnosis of metabolic dysfunction-associated steatotic liver disease (MASLD) and hepatic fibrosis, researchers reported. AACE President, Scott D. Isaacs, MD, FACP, FACE, shares his perspective on the study, highlighting its potential to improve MASLD diagnoses by providing noninvasive and cost-effective alternatives to liver biopsy. He emphasizes the need for further validation with liver biopsy data and integration into clinical practice could lead to earlier interventions, potentially reducing the progression of MASLD to more severe conditions like metabolic dysfunction-associated steatohepatitis (MASH) and cirrhosis. Read the full article on Healio to learn more.