Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
%0 Journal Article
%1 Nam2022-yt
%A Nam, David
%A Chapiro, Julius
%A Paradis, Valerie
%A Seraphin, Tobias Paul
%A Kather, Jakob Nikolas
%D 2022
%I Elsevier BV
%J JHEP Rep.
%K AI CNN Communications DICOM Diagnosis Digital HCC Imaging Individual ML MVI Medicine NAFLD NASH Prognosis Reporting TACE TRIPOD Transparent WSIs a and artificial carcinoma chemoembolisation convolutional data deep diagnostic disease fatty for hepatocellular images imaging in integration intelligence invasion learning liver machine microvascular model multimodal multivariable network neural non-alcoholic of or prediction slide steatohepatitis support system topic_lifescience transarterial whole zno
%N 4
%P 100443
%T Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
%V 4
%X Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.
@article{Nam2022-yt,
abstract = {Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of metabolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quantitative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hepatology and outline critical obstacles which need to be overcome.},
added-at = {2024-09-10T11:54:51.000+0200},
author = {Nam, David and Chapiro, Julius and Paradis, Valerie and Seraphin, Tobias Paul and Kather, Jakob Nikolas},
biburl = {https://puma.scadsai.uni-leipzig.de/bibtex/27cbae4f7253660fab9e73e9067c8cf9e/scadsfct},
copyright = {http://creativecommons.org/licenses/by/4.0/},
interhash = {121fb2ffded17f7e4376be51d3369156},
intrahash = {7cbae4f7253660fab9e73e9067c8cf9e},
journal = {JHEP Rep.},
keywords = {AI CNN Communications DICOM Diagnosis Digital HCC Imaging Individual ML MVI Medicine NAFLD NASH Prognosis Reporting TACE TRIPOD Transparent WSIs a and artificial carcinoma chemoembolisation convolutional data deep diagnostic disease fatty for hepatocellular images imaging in integration intelligence invasion learning liver machine microvascular model multimodal multivariable network neural non-alcoholic of or prediction slide steatohepatitis support system topic_lifescience transarterial whole zno},
language = {en},
month = apr,
number = 4,
pages = 100443,
publisher = {Elsevier BV},
timestamp = {2025-07-29T10:50:57.000+0200},
title = {Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction},
volume = 4,
year = 2022
}
