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Artificial Intelligence in Hepatology- Ready for the Primetime.

In Journal of clinical and experimental hepatology

Artificial Intelligence (AI) is a mathematical process of computer mediating designing of algorithms to support human intelligence. AI in hepatology has shown tremendous promise to plan appropriate management and hence improve treatment outcomes. The field of AI is in a very early phase with limited clinical use. AI tools such as machine learning, deep learning, and 'big data' are in a continuous phase of evolution, presently being applied for clinical and basic research. In this review, we have summarized various AI applications in hepatology, the pitfalls and AI's future implications. Different AI models and algorithms are under study using clinical, laboratory, endoscopic and imaging parameters to diagnose and manage liver diseases and mass lesions. AI has helped to reduce human errors and improve treatment protocols. Further research and validation are required for future use of AI in hepatology.

Kalapala Rakesh, Rughwani Hardik, Reddy D Nageshwar

2023

ACLF, acute on chronic liver failure, AI, artificial intelligence, ALD, alcoholic liver disease, ALT, alanine transaminase, ANN, artificial neural network, AST, aspartate aminotransferase, AUD, alcohol use disorder, CHB, chronic hepatitis B, CHC, chronic hepatitis C, CLD, chronic liver disease, CNN, convolutional neural network, DL, deep learning, FIB-4, fibrosis-4 score, GGTP, gamma glutamyl transferase, HCC, hepatocellular carcinoma, HDL, high density lipoprotein, ML, machine learning, MLR, multi-nomial logistic regressions, NAFLD, NAFLD, non-alcoholic fatty liver disease, NASH, non-alcoholic steatohepatitis, NLP, natural language processing, RF, random forest, RTE, real-time tissue elastography, SOLs, space-occupying lesions, SVM, support vector machine, artificial intelligence, deep learning, hepatology, machine learning