脂肪肝疾病目前影響全球約三成的成年人，且由於早期通常沒有明顯症狀，許多患者直到病情發展為危及生命的肝衰竭或肝硬化時才被診斷出來。然而，如果在早期發現，這種疾病造成的肝臟損傷是可以透過生活方式的改變或新型藥物來逆轉並恢復健康的。（關鍵數字：30）

為了解決早期診斷的困境，醫療專家開始探索人工智慧的應用，希望利用 AI 系統分析大量的電子健康紀錄、常規血液檢查數據以及胸部 X 光片，從中篩選出隱藏的肝臟脂肪堆積或纖維化高風險患者。這種自動化的背景分析能大幅減輕醫生的負擔，並及時將合適的患者轉介給專科醫師。

目前已有如 LiverPRO 與 ALADDIN 等多項 AI 演算法在研究中展現出優於傳統指標的準確度，能更精準地預測嚴重肝臟問題的風險並協助選擇適合特定藥物治療的病患。這些技術的普及不僅能減少不必要的侵入性檢查與轉診，更能透過早期發現與治療，為醫療系統省下龐大的開銷。

Fatty liver disease currently affects about 30 percent of adults worldwide, and because it usually presents no noticeable symptoms in its early stages, many patients are only diagnosed when the condition progresses to life-threatening liver failure or cirrhosis. However, if detected early, the liver damage caused by this disease is highly reversible through lifestyle changes or novel medications, allowing patients to regain their health.

To address the challenges of early diagnosis, medical experts are exploring the application of artificial intelligence, hoping to use AI systems to analyze vast amounts of electronic health records, routine blood test data, and chest x-rays to identify hidden high-risk patients with liver fat accumulation or fibrosis. This automated background analysis can significantly reduce doctors' workloads and promptly refer appropriate patients to specialists.

Several AI algorithms, such as LiverPRO and ALADDIN, have already demonstrated superior accuracy compared to traditional indexes in predicting the risk of serious liver problems and helping to select patients for specific drug treatments. The widespread adoption of these technologies can not only reduce unnecessary invasive tests and referrals but also save healthcare systems enormous costs through early detection and treatment.