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Verfasst von:Žigutytė, Laura [VerfasserIn]   i
 Sorz-Nechay, Thomas [VerfasserIn]   i
 Clusmann, Jan Niklas [VerfasserIn]   i
 Kather, Jakob Nikolas [VerfasserIn]   i
Titel:Use of artificial intelligence for liver diseases
Titelzusatz:a survey from the EASL congress 2024
Verf.angabe:Laura Žigutytė, Thomas Sorz-Nechay, Jan Clusmann, Jakob Nikolas Kather
E-Jahr:2024
Jahr:December 2024
Umfang:15 S.
Illustrationen:Illustrationen
Fussnoten:Online verfügbar: 06. September 2024, Artikelversion: 08. November 2024 ; Gesehen am 27.05.2025
Titel Quelle:Enthalten in: JHEP reports
Ort Quelle:Amsterdam : Elsevier, 2019
Jahr Quelle:2024
Band/Heft Quelle:6(2024), 12, Artikel-ID 101209, Seite 1-15
ISSN Quelle:2589-5559
Abstract: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.
DOI:doi:10.1016/j.jhepr.2024.101209
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

kostenfrei: Volltext: https://doi.org/10.1016/j.jhepr.2024.101209
 kostenfrei: Volltext: https://www.sciencedirect.com/science/article/pii/S2589555924002131
 DOI: https://doi.org/10.1016/j.jhepr.2024.101209
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:biomarkers
 Deep learning
 large language models
 liver cancer
 liver cirrhosis
 liver fibrosis
 machine learning
 MASLD
 medical data
 medical image analysis
K10plus-PPN:1926693965
Verknüpfungen:→ Zeitschrift

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