肝病学
医学
预言
医学物理学
背景(考古学)
内科学
人工智能
计算机科学
数据挖掘
生物
古生物学
作者
David Nam,Julius Chapiro,Valérie Paradis,Tobias Paul Seraphin,Jakob Nikolas Kather
出处
期刊:JHEP reports
[Elsevier]
日期:2022-04-01
卷期号:4 (4): 100443-100443
被引量:75
标识
DOI:10.1016/j.jhepr.2022.100443
摘要
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.
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