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Artificial intelligence for the management of pancreatic diseases
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Med...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898901/ https://www.ncbi.nlm.nih.gov/pubmed/33065754 http://dx.doi.org/10.1111/den.13875 |
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author | Gorris, Myrte Hoogenboom, Sanne A. Wallace, Michael B. van Hooft, Jeanin E. |
author_facet | Gorris, Myrte Hoogenboom, Sanne A. Wallace, Michael B. van Hooft, Jeanin E. |
author_sort | Gorris, Myrte |
collection | PubMed |
description | Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine‐learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models. |
format | Online Article Text |
id | pubmed-7898901 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78989012021-03-03 Artificial intelligence for the management of pancreatic diseases Gorris, Myrte Hoogenboom, Sanne A. Wallace, Michael B. van Hooft, Jeanin E. Dig Endosc Reviews Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine‐learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models. John Wiley and Sons Inc. 2020-12-05 2021-01 /pmc/articles/PMC7898901/ /pubmed/33065754 http://dx.doi.org/10.1111/den.13875 Text en © 2020 The Authors. Digestive Endoscopy published by John Wiley & Sons Australia, Ltd on behalf of Japan Gastroenterological Endoscopy Society. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Reviews Gorris, Myrte Hoogenboom, Sanne A. Wallace, Michael B. van Hooft, Jeanin E. Artificial intelligence for the management of pancreatic diseases |
title | Artificial intelligence for the management of pancreatic diseases |
title_full | Artificial intelligence for the management of pancreatic diseases |
title_fullStr | Artificial intelligence for the management of pancreatic diseases |
title_full_unstemmed | Artificial intelligence for the management of pancreatic diseases |
title_short | Artificial intelligence for the management of pancreatic diseases |
title_sort | artificial intelligence for the management of pancreatic diseases |
topic | Reviews |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898901/ https://www.ncbi.nlm.nih.gov/pubmed/33065754 http://dx.doi.org/10.1111/den.13875 |
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