Guidelines for conducting ethical AI research in neurology: Supplementary materials - Chiang, Sharon; Picard, Rosalind; Chiong, Winston; Moss, Robert; Worrell, Gregory; Rao, Vikram; Goldenholz, Daniel (Dryad, 2021)
Pre-emptive recognition of  the ethical implications of study
design and algorithm choices in artificial intelligence (AI) research is
an important but challenging process. AI applications have begun to
transition from a promising future to clinical reality in neurology. As
the clinical management of neurology is often concerned with discrete,
often unpredictable, and highly consequential events linked to multimodal
data streams over long timescales, forthcoming advances in AI have great
potential to transform care for patients. However, critical ethical
questions have been raised with implementation of the first AI
applications in clinical practice. Clearly, AI will have far-reaching
potential to promote, but also to endanger, ethical clinical practice.
This article employs an anticipatory ethics approach to scrutinize how
researchers in neurology can methodically identify ethical ramifications
of design choices early in the research and development process, with a
goal of pre-empting unintended consequences that may violate principles of
ethical clinical care. First, we discuss the use of a systematic framework
for researchers to identify ethical ramifications of various study design
and algorithm choices. Second, using epilepsy as a paradigmatic example,
anticipatory clinical scenarios that illustrate unintended ethical
consequences are discussed, and failure points in each scenario evaluated.
Third, we provide practical recommendations for understanding and
addressing ethical ramifications early in methods development stages.
Awareness of the ethical implications of study design and algorithm
choices that may unintentionally enter AI is crucial to ensuring that
incorporation of AI into neurology care leads to patient benefit rather
than harm.
Keywords: FOS: Medical biotechnology