Short communicationArtificial intelligence for breast cancer screening: Opportunity or hype?
Section snippets
Breast cancer screening
Most developed health care systems have implemented population screening for breast cancer (BC) based on evidence from randomised trials that mammography confers BC mortality reduction, complemented by observational evidence of benefit from real-world screening [1], [2]. BC screening involves interpretation of digital mammograms to identify suspicious abnormalities that warrant further investigation (recall to assessment) to rule in or rule out BC. Mammography interpretation is subjective. In
Artificial intelligence (AI)
Machine learning (ML), a rapidly growing field of AI, integrating computer science and statistics, allows computers to learn without explicit programming through automatic extraction and analysis of complex data [12], [13], [14], [15], [16], [17]. ML is being touted as a potential AI tool to help discover new materials, master various games, and improve predictive ability in clinical medicine. The convergence of new ML techniques, such as deep learning (a class of learning algorithms that
Exploration of AI for BC screening
Medical imaging is ripe for incorporating AI solutions because of existing capabilities in computer vision and automated image feature analysis, the large storage capacity for hundreds of thousands of digital imaging exams through picture archiving and communication systems (PACS), linkage of PACS to electronic medical records, and the binary outcome of imaging-based screening tests [10]. Recent work developing AI algorithms in digital mammography interpretation has undertaken the conversion of
Future research using AI for BC screening
Although research into the capability of AI in mammography screening at present focuses on interpretive accuracy with the anticipation of primarily reducing false-positives, there remains much opportunity to extend exploration of the role of AI in BC screening, for example to develop future models that also better identify BC. This would not be limited to potential detection of cancers ‘missed’ at human screen-reading (with the aim of reducing the frequency of interval cancers) but may
Unexplored aspects of AI for BC screening
A number of unexplored issues, highly relevant for the application of AI in BC screening, warrant consideration and research effort. These include the social and ethical concerns and implications inherent in entrusting cancer detection to an AI model, and the possibility of un-intended consequences [11]. These issues need to be explored early in the phase of developing AI models for BC screening to provide an understanding of societal perspectives, and to define an ethical-legal framework for
Conclusion
Whether the anticipated promise of AI in BC detection, or indeed more broadly in cancer research, translates into practice and meets expectations remains to be seen over the coming years. At present, however, AI represents an opportunity that is both feasible and timely for exploration in population BC screening. To make the most of this opportunity, extremely large data-sets of imaging examinations linked to clinical factors and cancer outcomes are needed to train and validate robust AI models
Financial disclosure
None to disclose.
Conflict of interest statement (COI)
None to declare.
Acknowledgements
N. Houssami receives research support through a National Breast Cancer Foundation (NBCF Australia), Breast Cancer Research Leadership Fellowship.
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