Liver disease, which is treatable when detected early, often goes unnoticed until it reaches advanced stages. Metabolic-associated steatotic liver disease (MASLD), the most prevalent form of liver disease, occurs when the liver is unable to properly manage fat, and it is commonly linked to conditions like obesity, Type-2 diabetes, and high cholesterol. Early detection of MASLD is crucial because it can rapidly progress to more severe liver conditions, but many individuals in the early stages show no symptoms, making diagnosis difficult. Now, a new study has demonstrated that an artificial intelligence (AI)-powered algorithm can accurately identify early-stage MASLD by analyzing electronic health records (EHRs).

Researchers at the University of Washington (Seattle, WA, USA) employed an AI algorithm to examine imaging data within EHRs from three sites in the University of Washington Medical System to identify patients who met the criteria for MASLD. Of the 834 patients identified, only 137 had a formal MASLD diagnosis recorded. This means that 83% of patients who met the criteria for MASLD were undiagnosed, despite the data in their EHRs indicating they were at risk.

“A significant proportion of patients who meet criteria for MASLD go undiagnosed. This is concerning because delays in early diagnosis increase the likelihood of progression to advanced liver disease,” said Ariana Stuart MD, a resident at University of Washington Internal Medicine Residency Program and lead author of the study. “People should not interpret our findings as a lack of primary care training or management. Instead, our study shows how AI can complement physician workflow to address the limitations of traditional clinical practice.” 

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