An overview of artificial intelligence applications for next-generation gynaecological pathology

Sarah Bell; James D. Blackwood; Christina Fell; Mahnaz Mohammadi; David Morrison; David Harris-Birtill; Gareth Bryson

doi:10.1016/j.mpdhp.2023.07.002

An overview of artificial intelligence applications for next-generation gynaecological pathology

Sarah Bell, James D. Blackwood, Christina Fell, Mahnaz Mohammadi, David Morrison, David Harris-Birtill, Gareth Bryson

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Abstract

With the drive to roll out digital pathology in the UK, implementation of artificial intelligence (AI) tools for pathology is now a possibility, bringing with it the potential to change how we work as a specialty. AI promises many benefits for working practices such as improved efficiency and consistency, financial and productivity gains and ultimately a better service for our patients. Gynaecological pathology is a diverse specialty with many potential avenues for algorithm development, yet there are relatively few nearing clinical validation compared to other pathology specialties. This article provides a summary of the current landscape of AI in pathology with a focus on applications in gynaecological pathology. We discuss the ways pathologists can be involved in algorithm development and draw on our significant experiences in a nationally funded programme for AI development and research. Finally we look to what the future might hold.

Original language	English
Pages (from-to)	442-449
Number of pages	8
Journal	Diagnostic Histopathology
Volume	29
Issue number	10
Early online date	26 Jul 2023
DOIs	https://doi.org/10.1016/j.mpdhp.2023.07.002
Publication status	Published - 1 Oct 2023

Keywords

Artificial intelligence
Computational pathology
Deep learning
Digital pathology
Gynaecological pathology
Whole slide images

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10.1016/j.mpdhp.2023.07.002

Bell_2023_DH_AIapplications_AAM
Copyright © 2023 the Publisher/the Authors. This work has been made available online in accordance with the Rights Retention Strategy This accepted manuscript is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The final published version of this work is available at https://doi.org/https://doi.org/10.1016/j.mpdhp.2023.07.002.
Accepted author manuscript, 1.95 MBLicence: CC BY

ICAIRD: I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics
Harris-Birtill, D. C. C. (PI) & Arandelovic, O. (CoI)
Technology Strategy Board
1/02/19 → 31/01/23
Project: Standard

Cite this

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title = "An overview of artificial intelligence applications for next-generation gynaecological pathology",

abstract = "With the drive to roll out digital pathology in the UK, implementation of artificial intelligence (AI) tools for pathology is now a possibility, bringing with it the potential to change how we work as a specialty. AI promises many benefits for working practices such as improved efficiency and consistency, financial and productivity gains and ultimately a better service for our patients. Gynaecological pathology is a diverse specialty with many potential avenues for algorithm development, yet there are relatively few nearing clinical validation compared to other pathology specialties. This article provides a summary of the current landscape of AI in pathology with a focus on applications in gynaecological pathology. We discuss the ways pathologists can be involved in algorithm development and draw on our significant experiences in a nationally funded programme for AI development and research. Finally we look to what the future might hold.",

keywords = "Artificial intelligence, Computational pathology, Deep learning, Digital pathology, Gynaecological pathology, Whole slide images",

author = "Sarah Bell and Blackwood, {James D.} and Christina Fell and Mahnaz Mohammadi and David Morrison and David Harris-Birtill and Gareth Bryson",

note = "Funding: The work described in the section Creation of a Scottish {\textquoteleft}living lab{\textquoteright} for AI development is part of iCAIRD which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI), project number 104690.",

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doi = "10.1016/j.mpdhp.2023.07.002",

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AU - Harris-Birtill, David

AU - Bryson, Gareth

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AB - With the drive to roll out digital pathology in the UK, implementation of artificial intelligence (AI) tools for pathology is now a possibility, bringing with it the potential to change how we work as a specialty. AI promises many benefits for working practices such as improved efficiency and consistency, financial and productivity gains and ultimately a better service for our patients. Gynaecological pathology is a diverse specialty with many potential avenues for algorithm development, yet there are relatively few nearing clinical validation compared to other pathology specialties. This article provides a summary of the current landscape of AI in pathology with a focus on applications in gynaecological pathology. We discuss the ways pathologists can be involved in algorithm development and draw on our significant experiences in a nationally funded programme for AI development and research. Finally we look to what the future might hold.

KW - Artificial intelligence

KW - Computational pathology

KW - Deep learning

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KW - Gynaecological pathology

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An overview of artificial intelligence applications for next-generation gynaecological pathology

Abstract

Keywords

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Projects

ICAIRD: I-CAIRD: Industrial Centre for AI Research in Digital Diagnostics

Cite this