Literature DB >> 34266794

The application of artificial intelligence in hepatology: A systematic review.

Clara Balsano1, Anna Alisi2, Maurizia R Brunetto3, Pietro Invernizzi4, Patrizia Burra5, Fabio Piscaglia6.   

Abstract

The integration of human and artificial intelligence (AI) in medicine has only recently begun but it has already become obvious that intelligent systems can dramatically improve the management of liver diseases. Big data made it possible to envisage transformative developments of the use of AI for diagnosing, predicting prognosis and treating liver diseases, but there is still a lot of work to do. If we want to achieve the 21st century digital revolution, there is an urgent need for specific national and international rules, and to adhere to bioethical parameters when collecting data. Avoiding misleading results is essential for the effective use of AI. A crucial question is whether it is possible to sustain, technically and morally, the process of integration between man and machine. We present a systematic review on the applications of AI to hepatology, highlighting the current challenges and crucial issues related to the use of such technologies.
Copyright © 2021. Published by Elsevier Ltd.

Entities:  

Keywords:  Artificial intelligence; Big data; Deep learning; Hepatology; Machine learning; liver diseases

Mesh:

Year:  2021        PMID: 34266794     DOI: 10.1016/j.dld.2021.06.011

Source DB:  PubMed          Journal:  Dig Liver Dis        ISSN: 1590-8658            Impact factor:   4.088


  2 in total

1.  Editorial: Clinical Application of Artificial Intelligence in Emergency and Critical Care Medicine, Volume I.

Authors:  Zhongheng Zhang; Nan Liu; Qinghe Meng; Longxiang Su
Journal:  Front Med (Lausanne)       Date:  2021-12-06

2.  Advancements of Artificial Intelligence in Liver-Associated Diseases and Surgery.

Authors:  Anas Taha; Vincent Ochs; Leos N Kayhan; Bassey Enodien; Daniel M Frey; Lukas Krähenbühl; Stephanie Taha-Mehlitz
Journal:  Medicina (Kaunas)       Date:  2022-03-22       Impact factor: 2.948

  2 in total

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