ArticleInsights into imaging2022
AI in breast screening mammography: breast screening readers' perspectives.
Article in Insights into imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Preferences for the Use of Artificial Intelligence for Breast Cancer Screening in Australia: A Discrete Choice Experiment.The patient · 2025Trial
- Artificial intelligence in breast cancer screening: A systematic review and meta-analysis of integration strategies.European journal of radiology open · 2026Review
- Counterfactual Reasoning for Mammogram Classification via Semantic Texture Masking.Journal of imaging informatics in medicine · 2026Article
- Prospective evaluation of artificial intelligence integration into breast cancer screening in multiple workflow settings: the GEMINI study.Nature cancer · 2026Article
- Patients' views on the use of artificial intelligence in healthcare: Artificial Intelligence Survey Aachen (AISA)-a prospective survey.Insights into imaging · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Radiologists' Perspectives on AI Integration in Mammographic Breast Cancer Screening: A Mixed Methods Study.Cancers · 2025Article
- Artificial Intelligence in Radiology: A Leadership Survey.Journal of the American College of Radiology : JACR · 2025Article
- Australian healthcare workers' views on artificial intelligence in BreastScreen: Results of a mixed method survey study.Preventive medicine reports · 2024Article
- AI-integrated Screening to Replace Double Reading of Mammograms: A Population-wide Accuracy and Feasibility Study.Radiology. Artificial intelligence · 2024Article
- Population-wide evaluation of artificial intelligence and radiologist assessment of screening mammograms.European radiology · 2024Article
- Patients' Perceptions and Attitudes to the Use of Artificial Intelligence in Breast Cancer Diagnosis: A Narrative Review.Life (Basel, Switzerland) · 2024Review
- Breast cancer detection accuracy of AI in an entire screening population: a retrospective, multicentre study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
objectivesThis study surveyed the views of breast screening readers in the UK on how to incorporate Artificial Intelligence (AI) technology into breast screening mammography.
methodsAn 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.
resultsThe 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.
conclusionsOverall, 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.