OBJECTIVES: This study surveyed the views of breast screening readers in the UK on how to incorporate Artificial Intelligence (AI) technology into breast screening mammography.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
We would like to thank all the survey respondents for their time and input. We would also like to thank the Scottish Breast Radiology Forum (SBRF) and British Society of Breast Radiology (BSBR) for their aid in dissemination of the survey. Furthermore, we would like to thank Dr Rumana Newlands for her advice on how to perform content analysis and report its results.
iCAIRD Radiology Collaboration team members:
Harrison D (iCAIRD Director), University of St Andrews. Black C, Murray A and Wilde K, University of Aberdeen. Blackwood JD, NHS Greater Glasgow and Clyde. Butterly C and Zurowski J, University of Glasgow. Eilbeck J and McSkimming C, NHS Grampian. Canon Medical Research Europe Ltd. – SHAIP platform.
This work is supported by the Industrial Centre for Artificial Intelligence Research in Digital Diagnostics (iCAIRD) which is funded by Innovate UK on behalf of UK Research and Innovation (UKRI) [project number: 104690]. The funding source was not involved in study design; collection, analysis and interpretation of data; writing of the report; or in the decision to submit the article for publication.
- Breast screening reader