Researchers have developed an artificial intelligence-powered blood test capable of detecting a common but often silent liver condition years before symptoms appear. The test analyzes routine blood samples using a machine learning algorithm to identify patterns indicative of metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease. This condition, which affects an estimated one in three adults globally, often progresses without noticeable symptoms until significant liver damage has occurred. The AI model was trained on a large dataset of patient records and can identify subtle biomarkers that traditional liver function tests might miss. Early detection through this non-invasive method could allow for lifestyle and medical interventions to prevent progression to more serious conditions like cirrhosis or liver cancer. The research, published in a peer-reviewed journal, represents a significant step toward population-level screening for a major public health concern. For the full details, read the complete article.