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AI in breast screening mammography: breast screening readers' perspectives
Clarisse Florence de Vries1, Samantha J Colosimo2,3, Moragh Boyle4
1Aberdeen Centre for Health Data Science, Institute of Applied Health Sciences, University of Aberdeen, Aberdeen, Scotland. clarisse.devries@abdn.ac.uk.
UK breast screening readers support Artificial Intelligence (AI) as a partial replacement for human readers in mammography. They prefer AI to indicate suspected tumor regions, emphasizing the need for national guidelines and further validation before implementation.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence in Healthcare
Background:
- Artificial Intelligence (AI) is increasingly explored for integration into medical diagnostics.
- Breast cancer screening mammography relies heavily on expert human readers.
Purpose of the Study:
- To survey UK breast screening readers' views on incorporating AI into mammography.
- To understand reader preferences for AI implementation scenarios and evidence requirements.
Main Methods:
- An online questionnaire was distributed to UK breast screening readers.
- Assessed approval of AI in triage, as a companion, replacing one reader, or replacing all readers.
- Ranked AI representation options and identified necessary supporting evidence.
Main Results:
- Respondents approved AI partially replacing human readers but objected to full replacement.
- A 'region of suspicion' was the preferred AI output visualization.
- National guidelines, nationally representative datasets, and independent prospective studies were deemed essential evidence.
Conclusions:
- Breast screening readers favor AI as a partial substitute for human readers.
- Graphical indication of suspected areas is preferred for AI output.
- Further evidence and established national guidelines are critical for AI adoption in mammography.
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