Diachronic and synchronic variation in the performance of adaptive machine learning systems: the ethical challenges.

ABSTRACT:

Objectives

Machine learning (ML) has the potential to facilitate "continual learning" in medicine, in which an ML system continues to evolve in response to exposure to new data over time, even after being deployed in a clinical setting. In this article, we provide a tutorial on the range of ethical issues raised by the use of such "adaptive" ML systems in medicine that have, thus far, been neglected in the literature.

Target audience

The target audiences for this tutorial are the developers of ML AI systems, healthcare regulators, the broader medical informatics community, and practicing clinicians.

Scope

Discussions of adaptive ML systems to date have overlooked the distinction between 2 sorts of variance that such systems may exhibit-diachronic evolution (change ove

SUBMITTER: Hatherley J 

PROVIDER: S-EPMC9846684 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature