Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
Research output: Contribution to journal › Review article › Contributed › peer-review
Contributors
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.
Details
Original language | English |
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Article number | 100443 |
Journal | JHEP Reports |
Volume | 4 |
Issue number | 4 |
Publication status | Published - Apr 2022 |
Peer-reviewed | Yes |
Externally published | Yes |
Keywords
ASJC Scopus subject areas
Keywords
- Artificial intelligence, deep learning, diagnostic support system, imaging, machine learning, multimodal data integration