Artificial Intelligence (AI) for the early detection of breast cancer: a scoping review to assess AI’s potential in breast screening practice

Abstract

Introduction: Various factors are driving interest in the application of artificial intelligence (AI) for breast cancer (BC) detection, but it is unclear whether the evidence warrants large-scale use in population-based screening. Areas covered: We performed a scoping review, a structured evidence synthesis describing a broad research field, to summarize knowledge on AI evaluated for BC detection and to assess AI's readiness for adoption in BC screening. Studies were predominantly small retrospective studies based on highly selected image datasets that contained a high proportion of cancers (median BC proportion in datasets 26.5%), and used heterogeneous techniques to develop AI models; the range of estimated AUC (area under ROC curve) for AI models was 69.2-97.8% (median AUC 88.2%). We identified various methodologic limitations including use of non-representative imaging data for model training, limited validation in external datasets, potential bias in training data, and few comparative data for AI versus radiologists' interpretation of mammography screening. Expert opinion: Although contemporary AI models have reported generally good accuracy for BC detection, methodological concerns, and evidence gaps exist that limit translation into clinical BC screening settings. These should be addressed in parallel to advancing AI techniques to render AI transferable to large-scale population-based screening.

Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study

“…First, the reader study was done with a cancer-enriched dataset, which has different cancer prevalence to real-field data (50% in the reader study dataset vs <1% in real-field data). Medical AI has been increasingly studied as deep learning technology becomes mainstream, but most studies have methodological deficiencies 15,16 -eg, in a systematic review of deep learning performance in medical imaging, only four of 82 studies considered diagnostic performance in an algorithm-plus-clinician scenario. 15 Although our study compared the diagnostic performance of humans, AI-aided humans, and standalone AI using an independent set of external data in a strict reader study format, the real clinical value needs to be investigated further via prospective clinical studies with the same prevalence of the real-world clinical setting.…”

Is the future of breast imaging with AI?

“…A review of the first studies of AI for breast cancer detection showed that these were predominantly retrospective studies based on relatively small and narrowly selected (i.e., cancer-enriched) imaging data sets as well as heterogeneous AI techniques. Most studies validated developed models, and rather few tested models on independent datasets [6].…”

“…AI algorithms should be expanded to cover other fields of breast imaging with huge amounts of data such as digital breast tomosynthesis, MRI, and automated 3D US. Moreover, future research will have to deal with the acceptability of using AI in breast cancer screening services and the many ethical, social, and legal implications of their use [6]. Computers cannot be held accountable, but radiologists applying AI algorithms for reporting will be.…”

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“…Houssami et al [45] exploring different AI methods in breast cancer detection. Authors are pinpoint the recent gaps in the clinical translation of AI systems into regular breast cancer screening practice.…”