More than a billion people worldwide are developing fatty liver disease, a condition now affecting roughly 30 percent of adults globally.
While a small amount of fat is normal, the organ’s weight exceeds 5 percent in many cases, often reaching 10 percent. This excess triggers inflammation, cell damage, and scar tissue formation known as fibrosis. The condition typically develops without noticeable symptoms, meaning it is rarely caught early. Even when cirrhosis or advanced scarring is present, three-quarters of patients are only diagnosed once the situation becomes life-threatening.
Progressive fat accumulation can lead to liver failure and increases the risk of cardiovascular disease and various cancers. Jeffrey Lazarus, a professor at the CUNY Graduate School of Public Health and Health Policy, suggests artificial intelligence could scan vast electronic health records to identify people who have accumulated worrying amounts of liver fat.
“AI can retrospectively go through massive numbers of hospital visits and lab reports,” says Lazarus. “You can use that to really prioritize who’s at most risk.”
Early identification allows much of the damage to reverse. Lifestyle changes such as reducing alcohol intake, losing weight through diet and exercise, and drinking more coffee have all been shown to reverse scarring and inflammation in initial stages. For those with moderate to advanced scarring, new treatments like the GLP-1 medication semaglutide and a drug called resmetirom have proven highly effective.
“The liver is a very versatile and interesting organ because it can regenerate, fibrosis can be reversed, and you can be completely healthy again,” says Lazarus. “But traditionally, we’ve focused more on late-stage care and trying to see how long we can keep a patient alive rather than finding them early and preventing the condition from advancing.”
Simple, noninvasive assessment methods exist but are rarely used, even in high-risk groups such as those with obesity and type 2 diabetes. The Fib-4 index calculates a score between 0 and 6 based on age, levels of two liver enzymes, and blood-clotting ability. This requires a liver blood test, often part of an annual medical checkup in the US. Doctors also have access to the enhanced liver fibrosis test, a more accurate second-line blood test measuring levels of two proteins involved in scar tissue creation and an enzyme that inhibits scar clearance.
Using both tests in patients with worrying amounts of liver fat improves the diagnosis of those with advanced fibrosis by four-fold. However, adding more testing to the workflow is not seen as sustainable for physicians facing a growing workload and administrative burden.
“You have to have something that can run in the background or it’s easy to just hit a button and do it,” says Jonathan Dranoff, a professor of medicine at Yale University.
As a result, Dranoff and Lazarus foresee a role for AI in automating Fib-4 score calculations from routine blood testing data. This would make it easier for primary care physicians to identify the right patients to refer to a liver specialist. AI could also use x-ray images. Last year, scientists at Osaka Metropolitan University in Japan published a study using an AI model to ingest and analyze routine chest x-ray scans. While primarily intended to examine the lungs and heart, these images also pick up parts of the liver. The researchers found that their model identified people with fatty liver disease with an accuracy of 82 percent.
Lazarus suggests AI-powered algorithms could be incorporated into all routine x-ray analyses where the liver is scanned alongside other organs. “The AI could pick up cases of excess liver fat, check for other risk factors such as if the person is overweight, has high cholesterol or type 2 diabetes, and then make a recommendation to the doctor,” says Lazarus. “It could tell them, ‘This might not have been what you were looking for, but this is what was picked up, and you should refer to hepatology or endocrinology who can do further tests.'”
Training AI algorithms on routine blood tests is also beginning to yield better diagnostics for progressive fatty liver disease. While Fib-4 is a quick and low-cost tool, it is less accurate in certain age groups, such as adolescents and seniors, and studies have raised concerns about rates of false positives and unnecessary referrals unless combined with additional testing.
As a result, the Danish health tech startup Evido has developed an AI-powered algorithm called LiverPRO. It assesses a patient’s risk of liver fibrosis based on age and nine routine blood-based biomarkers. Now being commercialized in partnership with the pharmaceutical company Roche, it has been shown to outperform Fib-4 in predicting risk of serious liver problems in more than 470,000 middle-aged people.
Other AI-based tests could also help doctors select the most appropriate patients for resmetirom treatment without requiring an invasive liver biopsy. Earlier this year, an international collective of hepatologists published the results of a study evaluating an AI model called ALADDIN. This machine learning algorithm, based on routine blood tests, showed it performed better than Fib-4 and other risk scores in identifying patients who could benefit most from the drug.
“These tools won’t completely replace biopsies or imaging,” says Paul Brennan, a specialty registrar in gastroenterology, hepatology, and internal medicine at the University of Dundee. “But they could fix the bottlenecks in primary care where most fibrosis goes undetected. I expect them to be adopted as a smarter first pass, catching the moderate-risk patients that blunter tools may miss, and reducing unnecessary referrals to hepatologists.”
So far, the use of AI in liver care has largely been confined to research, but Lazarus is optimistic that this will start to change soon. He points to research carried out in Denmark which found that informing people they have liver fibrosis makes them more likely to commit to dietary and exercise regimes. “We’re always looking to improve adherence to eating well and doing more physical activity,” says Lazarus. “Telling someone that they might have or do have liver disease is one way to do that.”
For health care systems grappling with a rising burden of chronic disease, identifying and treating patients in the earlier stages of fatty liver disease would save vast amounts of money.
“I would love to say, let’s go back through the electronic medical records across the various US health systems and find people before they have cirrhosis,” says Lazarus. “Liver transplants are extraordinarily expensive in any country, but especially the US. So there’s a lot of good humane and economic reasons to find people earlier on.”
What it means
Doctors will likely see more automated alerts in routine blood work and imaging results, prompting referrals before symptoms appear. Patients may receive clearer messages about their liver health, potentially improving their willingness to change lifestyle habits.
