Use of artificial intelligence for liver diseases

• Verfasst von: Žigutytė, Laura [VerfasserIn]
• Verfasst von: Sorz-Nechay, Thomas [VerfasserIn]
• Verfasst von: Clusmann, Jan Niklas [VerfasserIn]
• Verfasst von: Kather, Jakob Nikolas [VerfasserIn]
• 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
• DOI: doi:10.1016/j.jhepr.2024.101209
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: biomarkers
• Sach-SW: Deep learning
• Sach-SW: large language models
• Sach-SW: liver cancer
• Sach-SW: liver cirrhosis
• Sach-SW: liver fibrosis
• Sach-SW: machine learning
• Sach-SW: MASLD
• Sach-SW: medical data
• Sach-SW: medical image analysis
• K10plus-PPN: 1926693965

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.