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In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, with backgrounds in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision‐making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub‐kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI‐enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision‐making. [ABSTRACT FROM AUTHOR]

The goals of learning health systems (LHS) and of AI in medicine overlap in many respects. Both require significant improvements in data sharing and IT infrastructure, aim to provide more personalized care for patients, and strive to break down traditional barriers between research and care. However, the defining features of LHS and AI diverge when it comes to the people involved in medicine, both patients and providers. LHS aim to enhance physician‐patient relationships while developments in AI emphasize a physicianless experience. LHS also encourage better coordination of specialists across the health system, but AI aims to replace many specialists with technology and algorithms. This paper argues that these points of conflict may require a reconsideration of the role of humans in medical decision making. Although it is currently unclear to what extent machines will replace humans in healthcare, the parallel development of LHS and AI raises important questions about the exact role for humans within AI‐enabled healthcare. [ABSTRACT FROM AUTHOR]