Artificial intelligence in precision medicine in hepatology

Abstract

The advancement of investigation tools and electronic health records (EHR) enables a paradigm shift from guideline‐specific therapy toward patient‐specific precision medicine. The multiparametric and large detailed information necessitates novel analyses to explore the insight of diseases and to aid the diagnosis, monitoring, and outcome prediction. Artificial intelligence (AI), machine learning, and deep learning (DL) provide various models of supervised, or unsupervised algorithms, and sophisticated neural networks to generate predictive models more precisely than conventional ones. The data, application tasks, and algorithms are three key components in AI. Various data formats are available in daily clinical practice of hepatology, including radiological imaging, EHR, liver pathology, data from wearable devices, and multi‐omics measurements. The images of abdominal ultrasonography, computed tomography, and magnetic resonance imaging can be used to predict liver fibrosis, cirrhosis, non‐alcoholic fatty liver disease (NAFLD), and differentiation of benign tumors from hepatocellular carcinoma (HCC). Using EHR, the AI algorithms help predict the diagnosis and outcomes of liver cirrhosis, HCC, NAFLD, portal hypertension, varices, liver transplantation, and acute liver failure. AI helps to predict severity and patterns of fibrosis, steatosis, activity of NAFLD, and survival of HCC by using pathological data. Despite of these high potentials of AI application, data preparation, collection, quality, labeling, and sampling biases of data are major concerns. The selection, evaluation, and validation of algorithms, as well as real‐world application of these AI models, are also challenging. Nevertheless, AI opens the new era of precision medicine in hepatology, which will change our future practice.

Sarcopenia in hepatocellular carcinoma: Current knowledge and future directions

“…Precision medicine tools such as use of radiomics and radiogenomics are emerging for assessing host and tumor-related risk factors in HCC[ 106 , 107 ]. Radiomics uses medical imaging data to develop reproducible quantitative data from qualitative images.…”

“…A step further in this direction, use of genomics (cellular and molecular changes) to existing radiomics can provide radiogenomic information which can be used to develop molecular signatures for development for actionable clinical targets[ 107 ]. Finally use of artificial intelligence and deep learning can lead to next generation biostatistical and informatic data to develop algorithms and pathways to identify optimal clinical patterns[ 106 ].…”

Precision medicine in nonalcoholic fatty liver disease

“…Similarly, systems medicine approaches have the potential for promoting the development of (personalized and) precision medicine in various medical fields, notably including metabolic diseases, insulin resistance, obesity, and nonalcoholic fatty liver disease/nonalcoholic steatohepatitis NAFLD/NASH 2 . Shaped by big data technology (genomics, epigenomics, transcriptomics, proteomics, lipidomics, metabolomics, microbiomics, exposomics, and pharmacogenomics), precision medicine represents a shift of paradigm from medical practice directed by guidelines, to patient‐tailored approaches 3 …”

“…Progress in the AI field, by generating predictive models more precisely than conventional approached, promises to strongly improve the practice of precision medicine in hepatology 3 . For example, AI might use imaging data from various techniques (ultrasonography, computed tomography, and magnetic resonance) to sustain the differential diagnosis of both diffuse and focal liver disease 3 . Furthermore, the AI algorithms, using electronic health records and pathological data, may also anticipate NAFLD diagnosis and assess outcome trajectories 3 …”

A systematic review on application of deep learning in digestive system image processing

“…With the development of next-generation sequencing and multi-omics tools, precision medicine can help doctors more comprehensively understand the health status of patients [ 75 , 76 ]. In future, omics information can be integrated into imaging data to facilitate the development of precision medicine, provide professional health care strategies for patients with sub-health, and design the best diagnosis and treatment plan for patients [ 77 ] (Table 2 ).…”