Birch, Jonathan (2023) Medical AI, Inductive Risk, and the Communication of Uncertainty: The Case of Disorders of Consciousness. [Preprint]

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Abstract

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 (EEG) 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.

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Item Type:	Preprint
Creators:	Creators Email ORCID Birch, Jonathan j.birch2@lse.ac.uk 0000-0001-7517-4759
Keywords:	disorders of consciousness, cognitive-motor dissociation, inductive risk, AI and medicine
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Depositing User:	Dr Jonathan Birch
Date Deposited:	12 Jul 2023 12:20
Last Modified:	12 Jul 2023 12:20
Item ID:	22305
Subjects:	Specific Sciences > Artificial Intelligence > AI and Ethics Specific Sciences > Neuroscience > Cognitive Neuroscience Specific Sciences > Artificial Intelligence > Machine Learning General Issues > Values In Science
Date:	12 July 2023
URI:	https://philsci-archive.pitt.edu/id/eprint/22305

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