Mind the gap: AI and barriers to implementation in imaging-based screening

Abstract

With the emergence of artificial intelligence (AI) as a possible solution to the current workforce crises in the National Health Service, there has been an exponential increase in research showcasing its diagnostic performance in imaging-based screening programmes. It is likely to be implemented in screening in the near future. Surgeons play an integral role in any screening multidisciplinary team. However, there is a lack of awareness among the large majority regarding the evidence for AI in imaging-based diagnostics. It is of paramount importance that the surgeons who will be treating screen-detected cancers understand the nature of the AI models that will diagnose the disease. This review article outlines some of the flaws and gaps in the current AI-related evidence resulting in its lack of use at present. It describes four key stages to AI development, along with their respective barriers to implementation using examples from the breast, lung and orthopaedic fields. Furthermore, it explores algorithmic choice, big data, commonly reported outcome metrics and the novel human–AI relationship, together with possible solutions to ensure that AI is implemented safely in clinical practice.

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