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The Use of Digital Pathology and Artificial Intelligence in Histopathological Diagnostic Assessment of Prostate Cancer: A Survey of Prostate Cancer UK Supporters.

Authors :
Rakovic, Kai
Colling, Richard
Browning, Lisa
Dolton, Monica
Horton, Margaret R.
Protheroe, Andrew
Lamb, Alastair D.
Bryant, Richard J.
Scheffer, Richard
Crofts, James
Stanislaus, Ewart
Verrill, Clare
Source :
Diagnostics (2075-4418); May2022, Vol. 12 Issue 5, pN.PAG-N.PAG, 16p
Publication Year :
2022

Abstract

There has been particular interest in the deployment of digital pathology (DP) and artificial intelligence (AI) in the diagnosis of prostate cancer, but little is known about the views of the public on their use. Prostate Cancer UK supporters were invited to an online survey which included quantitative and qualitative questions exploring views on the use of DP and AI in histopathological assessment. A total of 1276 responses to the survey were analysed (response rate 12.5%). Most respondents were supportive of DP (87%, 1113/1276) and of testing AI in clinical practice as a diagnostic adjunct (83%, 1058/1276). Respondents saw DP as potentially increasing workflow efficiency, facilitating research, education/training and fostering clinical discussions between clinician and patient. Some respondents raised concerns regarding data security, reliability and the need for human oversight. Among those who were unsure about AI, information was requested regarding its performance and others wanted to defer the decision to use it to an expert. Although most are in favour of its use, some are unsure, and their concerns could be addressed with more information or better communication. A small minority (<1%) are not in favour of the testing of the use of AI in histopathology for reasons which are not easily addressed. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20754418
Volume :
12
Issue :
5
Database :
Complementary Index
Journal :
Diagnostics (2075-4418)
Publication Type :
Academic Journal
Accession number :
157191946
Full Text :
https://doi.org/10.3390/diagnostics12051225