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CA: A Cancer Journal for Clinicians

The research community has yet to reach a consensus on specific data sets that can be used for comparing and contrasting efforts in terms of performance, generalizability, and reproducibility, although the volume of medical data being made public is an encouraging move forward.206 Furthermore, access to available data sets should be improved to promote intellectual collaboration.

Institutional, professional, and government groups should be encouraged to share validated data to support the development of AI algorithms, which requires overcoming certain fundamental technical, legal, and perhaps ethical concerns.207 For example, the National Institutes of Health recently shared chest x‐ray and CT repositories to help AI scientists.208 Such efforts bear expansion to a much wider audience across disease states.

Although the current state of research has prioritized performance gains over explainability and transparency, the interpretability of AI is an active area of research.209 The benefits of trust and transparency in AI systems will differ based on their performance, allowing for the identification of failures when AI is subhuman and, consequently, transforming superhuman AI into a learning resource.

Finally, automated systems also might challenge the dynamics of responsibility within the doctor‐patient relationship, as well as the expectation of confidentiality.212 In terms of regulatory aspects, the US Food and Drug Administration has been regulating automated clinical decision‐making systems since the 1990s.213 With the advent of new prediction techniques, including deep learning, predictive models seeking approval must be further scrutinized in terms of the ground truth data used in training them, their intended use cases, and their generalizability and robustness against edge cases, as well as their life‐long learning aspects, as they are continuously updated with more learning and more data.

Current cyber security research starts to offer solutions, including cryptonets, in which homomorphic encryption allows neural networks to run training and inference on encrypted data.215 Today’s diagnostic paradigm in medicine focuses on the detection of visually recognizable findings that suggest discrete pathologies in images.

It is likely that, during the early phase of AI, when human experts will continue to play key roles in gatekeeping AI’s output, the majority of incidental findings detected by AI will still be evaluated by humans to discern whether or not they are clinically significant in the same manner as when humans detected incidental findings.

This shows initial promise in several disease conditions but requires additional proof of clinical utility in prospective trials and education of physicians, technologists, and physicists to incorporate into widespread use.216, 217 Although there likely will always be a “black box” for human experts in viewing AI‐generated results, data visualization tools are increasingly available to allow some degree of visual understanding of how algorithms make decisions.127 The curation of comprehensive data sets and outcomes that incorporate both disease‐related and unrelated elements also will help train and expand AI systems to account for risks beyond cancer itself.

Conversely, strategies that predict outcomes without a ground truth provided by human experts may disrupt the traditional workflow familiar to clinicians and patients today.218 Furthermore, the increased incorporation of AI in monitoring health resources and outcomes likely will improve efficiency and reduce cost.

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