Application of Artificial Intelligence for the Diagnosis and Treatment of Liver Diseases

Abstract

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.

Perspectives on Precision Medicine Approaches to NAFLD Diagnosis and Management

“…Also, in the future, the use of high-throughput omics (i.e., lipidomics proteomics, metabolomics, and glycomics) investigations will help to generate comprehensive biochemical snapshots allowing one to discriminate between patient subgroups [185,186]. Also, artificial intelligence tools to integrate and analyze big data as well as to develop algorithms combining the information through machine learning strategies [187][188][189] may be of help to better stratifying patients and defining tailored treatment strategies. A more accurate phenotyping of patients may also allow for grouping into more homogenous categories in clinical trials leading to more granular data on the efficacy of drugs in well-defined patient subgroups [26,190].…”

Deep learning-based detection of hepatobiliary disorders in ophthalmic imaging

“…In hepatology, artificial intelligence is being used to develop prognostic models, chart review tools using natural language processing, and deep learning-based software for interpretation of crosssectional imaging and histopathological images. 3 In ophthalmology, there have been successful applications of deep learning algorithms for diagnosis of ocular conditions such as glaucoma, diabetic retinopathy, and macular degeneration. 4 Nevertheless, this is the first study to propose an entirely new role for ophthalmological imaging to serve as a screening tool for early detection of hepatobiliary disorders.…”

Artificial intelligence in precision medicine in hepatology

“…ML is divided into supervised and unsupervised methods. Supervised learning deals with annotated data with input–output pairs and common techniques including linear regression, logistic regression, decision trees, k‐nearest neighbor, support vector machine (SVM), random forest (RF), naive Bayes classification, and gradient boosting 7 . Whereas unsupervised learning occurs when the task is to explore the groups without a priori knowledge within the data.…”

“…Whereas unsupervised learning occurs when the task is to explore the groups without a priori knowledge within the data. The k‐means clustering and principal component analysis are within this category 7 . In contrast to the traditional hypothesis‐driven statistical models, ML may use a hypothesis‐free approach to perform unsupervised classification of clinical phenotypes.…”