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Artificial intelligence in digital pathology : a roadmap to routine use in clinical practice
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CMPath AI in Histopathology Working Group (Including: Colling, Richard, Pitman, Helen, Oien, Karin, Rajpoot, Nasir M., Macklin, Philip, Snead, David, Sackville, Tony and Verrill, Clare). (2019) Artificial intelligence in digital pathology : a roadmap to routine use in clinical practice. Journal of Pathology, 249 (2). pp. 143-150. doi:10.1002/path.5310 ISSN 0022-3417.
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Official URL: https://doi.org/10.1002/path.5310
Abstract
The use of artificial intelligence will likely transform clinical practice over the next decade and the early impact of this will likely be the integration of image analysis and machine learning into routine histopathology. In the UK and around the world, a digital revolution is transforming the reporting practice of diagnostic histopathology and this has sparked a proliferation of image analysis software tools. While this is an exciting development that could discover novel predictive clinical information and potentially address international pathology work-force shortages, there is a clear need for a robust and evidence-based framework in which to develop these new tools in a collaborative manner that meets regulatory approval. With these issues in mind, the NCRI Cellular Molecular Pathology (CM-Path) initiative and the British in vitro Diagnostics Association (BIVDA) has set out a roadmap to help academia, industry and clinicians develop new software tools to the point of approved clinical use.
Item Type: | Journal Article | ||||||||
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Subjects: | R Medicine > R Medicine (General) R Medicine > RB Pathology |
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Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
Library of Congress Subject Headings (LCSH): | Medical informatics, Evidence-based medicine -- Technological innovations, Artificial intelligence -- Medical applications, Pathology -- Data processing, Diagnostic imaging -- Digital techniques, Evidence-based medicine | ||||||||
Journal or Publication Title: | Journal of Pathology | ||||||||
Publisher: | John Wiley & Sons Ltd. | ||||||||
ISSN: | 0022-3417 | ||||||||
Official Date: | October 2019 | ||||||||
Dates: |
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Volume: | 249 | ||||||||
Number: | 2 | ||||||||
Page Range: | pp. 143-150 | ||||||||
DOI: | 10.1002/path.5310 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Reuse Statement (publisher, data, author rights): | This is the peer reviewed version of the following article: Colling, Richard, Pitman, Helen, Oien, Karin, Rajpoot, Nasir M. (Nasir Mahmood), Macklin, Philip, Snead, David, Sackville, Tony and Verrill, Clare (2019) Artificial intelligence in digital pathology : a roadmap to routine use in clinical practice. Journal of Pathology, which has been published in final form at: https://doi.org/10.1002/path.5310 . This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 23 May 2019 | ||||||||
Date of first compliant Open Access: | 29 May 2020 | ||||||||
RIOXX Funder/Project Grant: |
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