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Artificial intelligence enabled applications in kidney disease
Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use...
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/PMC7891588/ https://www.ncbi.nlm.nih.gov/pubmed/32924202 http://dx.doi.org/10.1111/sdi.12915 |
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author | Chaudhuri, Sheetal Long, Andrew Zhang, Hanjie Monaghan, Caitlin Larkin, John W. Kotanko, Peter Kalaskar, Shashi Kooman, Jeroen P. van der Sande, Frank M. Maddux, Franklin W. Usvyat, Len A. |
author_facet | Chaudhuri, Sheetal Long, Andrew Zhang, Hanjie Monaghan, Caitlin Larkin, John W. Kotanko, Peter Kalaskar, Shashi Kooman, Jeroen P. van der Sande, Frank M. Maddux, Franklin W. Usvyat, Len A. |
author_sort | Chaudhuri, Sheetal |
collection | PubMed |
description | Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end‐stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists’ medical decision‐making, but instead assist them in providing optimal personalized care for their patients. |
format | Online Article Text |
id | pubmed-7891588 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78915882021-03-02 Artificial intelligence enabled applications in kidney disease Chaudhuri, Sheetal Long, Andrew Zhang, Hanjie Monaghan, Caitlin Larkin, John W. Kotanko, Peter Kalaskar, Shashi Kooman, Jeroen P. van der Sande, Frank M. Maddux, Franklin W. Usvyat, Len A. Semin Dial Review Articles Artificial intelligence (AI) is considered as the next natural progression of traditional statistical techniques. Advances in analytical methods and infrastructure enable AI to be applied in health care. While AI applications are relatively common in fields like ophthalmology and cardiology, its use is scarcely reported in nephrology. We present the current status of AI in research toward kidney disease and discuss future pathways for AI. The clinical applications of AI in progression to end‐stage kidney disease and dialysis can be broadly subdivided into three main topics: (a) predicting events in the future such as mortality and hospitalization; (b) providing treatment and decision aids such as automating drug prescription; and (c) identifying patterns such as phenotypical clusters and arteriovenous fistula aneurysm. At present, the use of prediction models in treating patients with kidney disease is still in its infancy and further evidence is needed to identify its relative value. Policies and regulations need to be addressed before implementing AI solutions at the point of care in clinics. AI is not anticipated to replace the nephrologists’ medical decision‐making, but instead assist them in providing optimal personalized care for their patients. John Wiley and Sons Inc. 2020-09-13 2021 /pmc/articles/PMC7891588/ /pubmed/32924202 http://dx.doi.org/10.1111/sdi.12915 Text en © 2020 The Authors. Seminars in Dialysis published by Wiley Periodicals LLC. 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 | Review Articles Chaudhuri, Sheetal Long, Andrew Zhang, Hanjie Monaghan, Caitlin Larkin, John W. Kotanko, Peter Kalaskar, Shashi Kooman, Jeroen P. van der Sande, Frank M. Maddux, Franklin W. Usvyat, Len A. Artificial intelligence enabled applications in kidney disease |
title | Artificial intelligence enabled applications in kidney disease |
title_full | Artificial intelligence enabled applications in kidney disease |
title_fullStr | Artificial intelligence enabled applications in kidney disease |
title_full_unstemmed | Artificial intelligence enabled applications in kidney disease |
title_short | Artificial intelligence enabled applications in kidney disease |
title_sort | artificial intelligence enabled applications in kidney disease |
topic | Review Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7891588/ https://www.ncbi.nlm.nih.gov/pubmed/32924202 http://dx.doi.org/10.1111/sdi.12915 |
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