Global Health & Medicine 2026;8(3):154-160.

Beyond consent: Reconstructing ethical justification in medical adaptive machine learning systems

Yamamoto K, Udagawa M, Nakazawa E

Medical Adaptive Machine Learning Systems (MAMLS) that continuously update their models using clinical data blur the conventional boundary between therapy and research, prompting the argument that their use should be classified as research and governed by informed consent requirements. Although informed consent remains normatively and legally important, this paper contends that consent-centered ethics faces two structural limitations in the context of MAMLS. First, the irreversibility inherent in deep learning models substantially undermines withdrawability—an important ancillary right of consent—thereby suggesting that consent may be transformed from an instrument of ongoing self-determination into a form of delegation to institutions. Second, the problem of data representativeness and bias shifts the unit of ethical analysis from the individual to the population, creating an "autonomy dilemma" in which respect for individual consent can paradoxically undermine the protection of autonomy at the collective level. Under these conditions, ethical justification must be complemented by, and in some contexts repositioned toward, public trust in institutions. The paper concludes that the ethical challenges surrounding MAMLS cannot be adequately addressed within the framework of research ethics alone, but must instead be taken up within the broader framework of public health ethics, with particular attention to transparency, accountability, and participatory governance.

DOI: 10.35772/ghm.2026.01047

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