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Bridging the AI Chasm: Can EBM Address Representation and Fairness in Clinical Machine Learning?

example, validation mechanisms used in the silent period reveal that the current accepted clinical practice or treatment disadvantages some groups. In this hypothetical scenario, the ML device might fail to create clinical equipoise required of the silent period not because of its own limitations, but due to the limitations and inequities in current clinical practice. Also, the reframing of the AI chasm also points to another set of differences in goals and priorities between ML, clinical science, and research ethics. As the authors note, “the ultimate goal of many ML tools in healthcare is the improvement of the health outcomes of patients,” whereas clinical science aims to “improve the care of patients,” and research ethics prioritizes “ethical rigor and protection of individuals” (9). Improving health outcomes, improving care, and ethical rigor and protection are related, but not the same. Because goal of research ethics is not to improve the health outcomes or care of patients who participate in biomedical research, does the governance of ML tools within a research ethics pathway prompt new considerations of therapeutic misconception or increasingly blur the line between care and research? Finally, McCradden et al. provide an example of how the examination of the ethics of ML in health can also contribute to scholarship on the ethics of translational research. For example, Hostiuc et al. argue for an expanded view of the ethics of translational research that builds upon, but is distinct from bioethics. They argue: “[t]he ethics of translational research should go beyond the classical topic of research ethics, or medical ethics. It should not only analyze the ethical issues that can be directly derived from the translational phases but also those derived from the gaps between translational phases (Hostiuc et al 2016, 8).” This piece was written before ML has the prominence that it does now in medicine, and the target article prompts us to consider whether the increasing entry of ML into health care raises ethical issues not only within the phases, but also what, if any, new kinds of ethical issues can emerge in the “gaps” between research ethics phases.