Use of artificial intelligence for liver diseases: a survey from the EASL congress 2024
Artificial intelligence (AI) methods enable humans to analyse large amounts of data, which would otherwise not be feasibly quantifiable. This is especially true for unstructured visual and textual data, which can contain invaluable insights into disease. The hepatology research landscape is complex...
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| Main Authors: | , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
December 2024
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| In: |
JHEP reports
Year: 2024, Volume: 6, Issue: 12, Pages: 1-15 |
| ISSN: | 2589-5559 |
| DOI: | 10.1016/j.jhepr.2024.101209 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1016/j.jhepr.2024.101209 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2589555924002131 
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| Author Notes: | Laura Žigutytė, Thomas Sorz-Nechay, Jan Clusmann, Jakob Nikolas Kather |
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| 245 | 1 | 0 | |a Use of artificial intelligence for liver diseases |b a survey from the EASL congress 2024 |c Laura Žigutytė, Thomas Sorz-Nechay, Jan Clusmann, Jakob Nikolas Kather |
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| 520 | |a Artificial intelligence (AI) methods enable humans to analyse large amounts of data, which would otherwise not be feasibly quantifiable. This is especially true for unstructured visual and textual data, which can contain invaluable insights into disease. The hepatology research landscape is complex and has generated large amounts of data to be mined. Many open questions can potentially be addressed with existing data through AI methods. However, the field of AI is sometimes obscured by hype cycles and imprecise terminologies. This can conceal the fact that numerous hepatology research groups already use AI methods in their scientific studies. In this review article, we aim to assess the contemporaneous use of AI methods in hepatology in Europe. To achieve this, we systematically surveyed all scientific contributions presented at the EASL Congress 2024. Out of 1,857 accepted abstracts (1,712 posters and 145 oral presentations), 6 presentations (∼4%) and 69 posters (∼4%) utilised AI methods. Of these, 55 posters were included in this review, while the others were excluded due to missing posters or incomplete methodologies. Finally, we summarise current academic trends in the use of AI methods and outline future directions, providing guidance for scientific stakeholders in the field of hepatology. | ||
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