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Translational AI and Deep Learning in Diagnostic Pathology

There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the dif...

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Autores principales: Serag, Ahmed, Ion-Margineanu, Adrian, Qureshi, Hammad, McMillan, Ryan, Saint Martin, Marie-Judith, Diamond, Jim, O'Reilly, Paul, Hamilton, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779702/
https://www.ncbi.nlm.nih.gov/pubmed/31632973
http://dx.doi.org/10.3389/fmed.2019.00185
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author Serag, Ahmed
Ion-Margineanu, Adrian
Qureshi, Hammad
McMillan, Ryan
Saint Martin, Marie-Judith
Diamond, Jim
O'Reilly, Paul
Hamilton, Peter
author_facet Serag, Ahmed
Ion-Margineanu, Adrian
Qureshi, Hammad
McMillan, Ryan
Saint Martin, Marie-Judith
Diamond, Jim
O'Reilly, Paul
Hamilton, Peter
author_sort Serag, Ahmed
collection PubMed
description There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe.
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spelling pubmed-67797022019-10-18 Translational AI and Deep Learning in Diagnostic Pathology Serag, Ahmed Ion-Margineanu, Adrian Qureshi, Hammad McMillan, Ryan Saint Martin, Marie-Judith Diamond, Jim O'Reilly, Paul Hamilton, Peter Front Med (Lausanne) Medicine There has been an exponential growth in the application of AI in health and in pathology. This is resulting in the innovation of deep learning technologies that are specifically aimed at cellular imaging and practical applications that could transform diagnostic pathology. This paper reviews the different approaches to deep learning in pathology, the public grand challenges that have driven this innovation and a range of emerging applications in pathology. The translation of AI into clinical practice will require applications to be embedded seamlessly within digital pathology workflows, driving an integrated approach to diagnostics and providing pathologists with new tools that accelerate workflow and improve diagnostic consistency and reduce errors. The clearance of digital pathology for primary diagnosis in the US by some manufacturers provides the platform on which to deliver practical AI. AI and computational pathology will continue to mature as researchers, clinicians, industry, regulatory organizations and patient advocacy groups work together to innovate and deliver new technologies to health care providers: technologies which are better, faster, cheaper, more precise, and safe. Frontiers Media S.A. 2019-10-01 /pmc/articles/PMC6779702/ /pubmed/31632973 http://dx.doi.org/10.3389/fmed.2019.00185 Text en Copyright © 2019 Serag, Ion-Margineanu, Qureshi, McMillan, Saint Martin, Diamond, O'Reilly and Hamilton. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Serag, Ahmed
Ion-Margineanu, Adrian
Qureshi, Hammad
McMillan, Ryan
Saint Martin, Marie-Judith
Diamond, Jim
O'Reilly, Paul
Hamilton, Peter
Translational AI and Deep Learning in Diagnostic Pathology
title Translational AI and Deep Learning in Diagnostic Pathology
title_full Translational AI and Deep Learning in Diagnostic Pathology
title_fullStr Translational AI and Deep Learning in Diagnostic Pathology
title_full_unstemmed Translational AI and Deep Learning in Diagnostic Pathology
title_short Translational AI and Deep Learning in Diagnostic Pathology
title_sort translational ai and deep learning in diagnostic pathology
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779702/
https://www.ncbi.nlm.nih.gov/pubmed/31632973
http://dx.doi.org/10.3389/fmed.2019.00185
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