Artificial Intelligence in Mammography Screening in Norway (AIMS Norway): Protocol for a randomized controlled trial

Abstract

Abstract Background and Objective Increasing screening volumes, combined with global shortage of radiologists and a high proportion of normal mammograms, challenge the efficiency and sustainability of breast cancer screening. Artificial intelligence (AI) has the potential to improve resource allocation, workflow efficiency and diagnostic performance by supporting and partially replacing radiologists in the interpretation process. This randomized, controlled, parallel-group, non-inferiority, single-blinded trial evaluates whether an AI-supported reading strategy, involving one or two radiologists depending on AI risk stratification, is non-inferior to standard independent double reading. The primary outcome is the number of screen-detected breast cancer cases in each group. Methods Women invited to BreastScreen Norway in the Western, Central, and Northern Norway Regional Health Authorities are eligible for inclusion. Following written informed consent, participants are randomized 1:1 to the control group (standard independent double reading by two radiologists) or the intervention group. In the intervention group, mammograms are analyzed using Transpara. Examinations with AI scores of 1–7 are interpreted by a single radiologist, whereas examinations with scores of 8–10 undergo independent double reading. Radiologists are blinded to AI scores and AI image markings during the initial interpretation; this information is disclosed during consensus meetings. Non-inferiority will be assessed by estimating confidence interval for the difference in screen-detected cancer rates between groups. Non-inferiority will be concluded if the upper bound of the confidence interval does not exceed the predefined non-inferiority margin. Conclusions The trial addresses a critical challenge in breast cancer screening: maintaining diagnostic performance while improving efficiency in the context of workforce constraints and a high prevalence of normal examinations. By evaluating a risk-stratified AI-supported reading strategy within a population-based screening program, the study will provide important evidence on whether AI can be safely integrated to optimize workload distribution while preserving cancer detection rates. Trial registration The ClinicalTrials.gov registry ( NCT06032390 )

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