Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice
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 rep...
| Autores principales: | , , , , , , , , |
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| Formato: | Journal article |
| Lenguaje: | English |
| Publicado: |
Wiley
2019
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| _version_ | 1826302934798827520 |
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| author | Colling, R Pitman, H Oien, K Rajpoot, N Macklin, P Snead, D Sackville, T Verrill, C CM-Path AI in Histopathology Working Group |
| author_facet | Colling, R Pitman, H Oien, K Rajpoot, N Macklin, P Snead, D Sackville, T Verrill, C CM-Path AI in Histopathology Working Group |
| author_sort | Colling, R |
| collection | OXFORD |
| description | 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. This article is protected by copyright. All rights reserved. |
| first_indexed | 2024-03-07T05:54:59Z |
| format | Journal article |
| id | oxford-uuid:ea38330e-083b-4979-9b92-6bb671d5f0e5 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:54:59Z |
| publishDate | 2019 |
| publisher | Wiley |
| record_format | dspace |
| spelling | oxford-uuid:ea38330e-083b-4979-9b92-6bb671d5f0e52022-03-27T11:00:17ZArtificial intelligence in digital pathology: A roadmap to routine use in clinical practiceJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:ea38330e-083b-4979-9b92-6bb671d5f0e5EnglishSymplectic Elements at OxfordWiley2019Colling, RPitman, HOien, KRajpoot, NMacklin, PSnead, DSackville, TVerrill, CCM-Path AI in Histopathology Working GroupThe 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. This article is protected by copyright. All rights reserved. |
| spellingShingle | Colling, R Pitman, H Oien, K Rajpoot, N Macklin, P Snead, D Sackville, T Verrill, C CM-Path AI in Histopathology Working Group Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title | Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title_full | Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title_fullStr | Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title_full_unstemmed | Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title_short | Artificial intelligence in digital pathology: A roadmap to routine use in clinical practice |
| title_sort | artificial intelligence in digital pathology a roadmap to routine use in clinical practice |
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