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General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology

Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, an...

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Detalles Bibliográficos
Autores principales: Makhlouf, Yasmine, Salto-Tellez, Manuel, James, Jacqueline, O’Reilly, Paul, Maxwell, Perry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141041/
https://www.ncbi.nlm.nih.gov/pubmed/35626427
http://dx.doi.org/10.3390/diagnostics12051272
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author Makhlouf, Yasmine
Salto-Tellez, Manuel
James, Jacqueline
O’Reilly, Paul
Maxwell, Perry
author_facet Makhlouf, Yasmine
Salto-Tellez, Manuel
James, Jacqueline
O’Reilly, Paul
Maxwell, Perry
author_sort Makhlouf, Yasmine
collection PubMed
description Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
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spelling pubmed-91410412022-05-28 General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology Makhlouf, Yasmine Salto-Tellez, Manuel James, Jacqueline O’Reilly, Paul Maxwell, Perry Diagnostics (Basel) Article Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product. MDPI 2022-05-20 /pmc/articles/PMC9141041/ /pubmed/35626427 http://dx.doi.org/10.3390/diagnostics12051272 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Makhlouf, Yasmine
Salto-Tellez, Manuel
James, Jacqueline
O’Reilly, Paul
Maxwell, Perry
General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title_full General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title_fullStr General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title_full_unstemmed General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title_short General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology
title_sort general roadmap and core steps for the development of ai tools in digital pathology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9141041/
https://www.ncbi.nlm.nih.gov/pubmed/35626427
http://dx.doi.org/10.3390/diagnostics12051272
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