TY - JOUR
T1 - Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases
AU - Ahn, Joseph C.
AU - Connell, Alistair
AU - Simonetto, Douglas A.
AU - Hughes, Cian
AU - Shah, Vijay H.
N1 - Publisher Copyright:
© 2020 by the American Association for the Study of Liver Diseases.
PY - 2021/6
Y1 - 2021/6
N2 - Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
AB - Modern medical care produces large volumes of multimodal patient data, which many clinicians struggle to process and synthesize into actionable knowledge. In recent years, artificial intelligence (AI) has emerged as an effective tool in this regard. The field of hepatology is no exception, with a growing number of studies published that apply AI techniques to the diagnosis and treatment of liver diseases. These have included machine-learning algorithms (such as regression models, Bayesian networks, and support vector machines) to predict disease progression, the presence of complications, and mortality; deep-learning algorithms to enable rapid, automated interpretation of radiologic and pathologic images; and natural-language processing to extract clinically meaningful concepts from vast quantities of unstructured data in electronic health records. This review article will provide a comprehensive overview of hepatology-focused AI research, discuss some of the barriers to clinical implementation and adoption, and suggest future directions for the field.
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U2 - 10.1002/hep.31603
DO - 10.1002/hep.31603
M3 - Review article
C2 - 33098140
AN - SCOPUS:85107948969
SN - 0270-9139
VL - 73
SP - 2546
EP - 2563
JO - Hepatology
JF - Hepatology
IS - 6
ER -