Review demonstrates clinical artificial intelligence applications in patients with viral hepatitis, highlighting diagnostic improvements alongside algorithmic bias challenges.
Recent advancements in AI have revealed novel perspectives in understanding, diagnosing, and managing liver diseases (4).This comprehensive review aims to provide an in-depth exploration of the various applications of AI in hepatology, elucidating its pivotal role in disease diagnosis, tailoring individualized treatments, optimizing decision-making, predicting patient outcomes, and facilitating continuous monitoring.Let us start by delving into the foundational principles of AI, data sourcing, and preprocessing.By delving into these principles, we can gain a deeper insight into how these technologies can ABSTRACT Artificial intelligence (AI) is rapidly transforming the field of hepatology, offering promising solutions for the diagnosis and treatment management of viral hepatitis.This review examines the various applications of AI in hepatology, including detection of liver fibrosis, cirrhosis, and hepatocellular carcinoma (HCC) using radiological (ultrasound, computed tomography, and magnetic resonance imaging) and pathological images, identification of individuals at high risk for viral hepatitis and its complications early identification of liver diseases through analysis of electronic health record data prediction of prognosis in HCC.Despite the remarkable potential of AI in hepatology, several challenges remain.Ethical concerns regarding data privacy, algorithmic biases, and regulatory compliance must be addressed.Collaborative efforts between healthcare professionals and data scientists are essential to navigate these challenges and unlock the full potential of AI in transforming hepatology.
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Tayibe Bal (2024) studied this question.
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