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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...

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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
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.
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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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