RT Journal Article SR Electronic T1 Medical AI, inductive risk and the communication of uncertainty: the case of disorders of consciousness JF Journal of Medical Ethics JO J Med Ethics FD BMJ Publishing Group Ltd and Institute of Medical Ethics SP jme-2023-109424 DO 10.1136/jme-2023-109424 A1 Birch, Jonathan YR 2023 UL http://jme.bmj.com/content/early/2023/11/17/jme-2023-109424.abstract AB Some patients, following brain injury, do not outwardly respond to spoken commands, yet show patterns of brain activity that indicate responsiveness. This is ‘cognitive-motor dissociation’ (CMD). Recent research has used machine learning to diagnose CMD from electroencephalogram recordings. These techniques have high false discovery rates, raising a serious problem of inductive risk. It is no solution to communicate the false discovery rates directly to the patient’s family, because this information may confuse, alarm and mislead. Instead, we need a procedure for generating case-specific probabilistic assessments that can be communicated clearly. This article constructs a possible procedure with three key elements: (1) A shift from categorical ‘responding or not’ assessments to degrees of evidence; (2) The use of patient-centred priors to convert degrees of evidence to probabilistic assessments; and (3) The use of standardised probability yardsticks to convey those assessments as clearly as possible.Data sharing is not applicable as no data sets were generated and/or analysed for this study.