Multi-model applications and cutting-edge advancements of artificial intelligence in hepatology in the era of precision medicine
Year: 2025
Type: article
Abstract: Hepatology encompasses various aspects, such as metabolic-associated fatty liver disease, viral hepatitis, alcoholic liver disease, liver cirrhosis, liver failure, liver tumors, and liver transplantation. The global epidemiological situation of liver diseases is grave, posing a substantial threat to human health and quality of life. Characterized by high incidence and mortality rates, liver diseases have emerged as a prominent global public health concern. In recent years, the rapid advancement of artificial intelligence (AI), deep learning, and radiomics has transformed medical research and clinical practice, demonstrating considerable potential in hepatology. AI is capable of automatically detecting abnormal cells in liver tissue sections, enhancing the accuracy and efficiency of pathological diagnosis. Deep learning models are able to extract features from computed tomography and magnetic resonance imaging images to facilitate liver disease classification. Machine learning models are capable of integrating clinical data to forecast disease progression and treatment responses, thus supporting clinical decision-making for personalized medicine. Through the analysis of imaging data, laboratory results, and genomic information, AI can assist in diagnosis, forecast disease progression, and optimize treatment plans, thereby improving clinical outcomes for liver disease patients. This minireview intends to comprehensively summarize the state-of-the-art theories and applications of AI in hepatology, explore the opportunities and challenges it presents in clinical practice, basic research, and translational medicine, and propose future research directions to guide the advancement of hepatology and ultimately improve patient outcomes.
Authors: Ying Zheng, Han Li, Ru Wang, Cong-Shan Jiang, Yi-Tong Zhao
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Funders: Australian Government, National Institute for Health and Care Research, Department of Health and Social Care, European Commission, Deutscher Akademischer Austauschdienst, Deutsche Forschungsgemeinschaft, Bundesministerium für Bildung und Forschung, Deutsche Krebshilfe, RWTH Aachen University, Gemeinsame Bundesausschuss, National Institutes of Health, HORIZON EUROPE Framework Programme, Leeds Biomedical Research Centre, National Heart, Lung, and Blood Institute, National Institute of Diabetes and Digestive and Kidney Diseases, National Center for Advancing Translational Sciences
Awards: G1316440902, ERA-NET Transcan - Tangerine - Künstliche Intelligenz-basierte End-to-End-Vorhersage des Ansprechens auf Krebsimmuntherapie, G1370780335, G1494183008, G1622551803, G1624360258, G1949475607, G2280242152, G2323685147, CAMINO - Programm zur Karriereentwicklung für forschende Ärztinnen und Ärzte in der multidimensionalen Tumorforschung, G237053356, G2654538971, G2669879858, G2820917408, Understanding Gene ENvironment Interaction in ALcohol-related hepatocellular carcinoma, G3570765224, G3815489264, G3848825097, Open Consortium for Decentralized Medical Artificial Intelligence, LiSyM-Krebs - Phase II - Verbundprojekt: DEEP-HCC - Vertieftes Systemverständnis der Entstehung des hepatozellulären Karzinoms als Grundlage für neue Früherkennungsmethoden - Teilprojekt A, G4617712253, Insights_eHealth - Interaktive Einblicke in Daten, Prozesse und Wissen im Bereich eHealth - Partner Kiel, G4712284355, G4785148476, G4881028819, G5089267161, G5233651463, Verbundprojekt: Competence Center for Interdisciplinary Data Sciences - Come2Data, Teilvorhaben: Lern, Unterstützungs- und Forschungsort TU Dresden, DECIPHER-M - Entschlüsselung von Metastasierung mit multimodalen Modellen der Künstlichen Intelligenz - Technische Universität Dresden, G6098565189, G6121262406, G6266899304, G6697820746, G7153114145, G7155641737, G7198334374, G745677937, G7501542120, G7562742651, SFB 1382: Die DarmLeber Achse – Funktionelle Zusammenhänge und therapeutische Strategien, G8150884884, Verbund PEARL - Prävention von Darmkrebs im jungen und mittleren Erwachsenenalter - Standort Dresden, G8310748285, CompLS - Runde 5 - Verbundprojekt: TRANSFORM LIVER - Weiterentwicklung von Vision Transformern zur Entdeckung von Biomarkern bei Lebererkrankungen - Teilprojekt A, G9000037725, G9000048485, G9000068233, G9000107429, G9000144006, G9000156230, G9000254524, G9000314692, San Diego Digestive Diseases Research Center, San Diego Cirrhosis Clinical Research Network, G993036999
OpenAlex ID: W4415389533