AI in breast screening mammography: breast screening readers' perspectives.

de Vries, Clarisse Florence ORCID: 0000-0002-9616-4231, Colosimo, Samantha J, Boyle, Moragh ORCID: 0000-0003-1947-7995, Lip, Gerald ORCID: 0000-0002-7566-1626, Anderson, Lesley A ORCID: 0000-0002-1000-3649, Staff, Roger T and iCAIRD Radiology Collaboration, (2022) AI in breast screening mammography: breast screening readers' perspectives. Insights into imaging, 13 (1). 186-.

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Official URL: http://dx.doi.org/10.1186/s13244-022-01322-4

Abstract

<h4>Objectives</h4>This study surveyed the views of breast screening readers in the UK on how to incorporate Artificial Intelligence (AI) technology into breast screening mammography.<h4>Methods</h4>An online questionnaire was circulated to the UK breast screening readers. Questions included their degree of approval of four AI implementation scenarios: AI as triage, AI as a companion reader/reader aid, AI replacing one of the initial two readers, and AI replacing all readers. They were also asked to rank five AI representation options (discrete opinion; mammographic scoring; percentage score with 100% indicating malignancy; region of suspicion; heat map) and indicate which evidence they considered necessary to support the implementation of AI into their practice among six options offered.<h4>Results</h4>The survey had 87 nationally accredited respondents across the UK; 73 completed the survey in full. Respondents approved of AI replacing one of the initial two human readers and objected to AI replacing all human readers. Participants were divided on AI as triage and AI as a reader companion. A region of suspicion superimposed on the image was the preferred AI representation option. Most screen readers considered national guidelines (77%), studies using a nationally representative dataset (65%) and independent prospective studies (60%) as essential evidence. Participants' free-text comments highlighted concerns and the need for additional validation.<h4>Conclusions</h4>Overall, screen readers supported the introduction of AI as a partial replacement of human readers and preferred a graphical indication of the suspected tumour area, with further evidence and national guidelines considered crucial prior to implementation.

Item Type:	Article
Uncontrolled Keywords:	iCAIRD Radiology Collaboration
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	10 Aug 2023 13:52
Last Modified:	10 Aug 2023 13:52
DOI:	10.1186/s13244-022-01322-4
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3172125