Published online by Cambridge University Press: 18 November 2022
Substantial advancement in the diagnosis and treatment of psychiatric disorders may come from assembling diverse data streams from clinical notes, neuroimaging, genetics, and real-time digital footprints from smartphones and wearable devices. This is called “deep phenotyping” and often involves machine learning. We argue that incidental findings arising in deep phenotyping research have certain special, morally and legally salient features: They are specific, actionable, numerous, and probabilistic. We consider ethical and legal implications of these features and propose a practical ethics strategy for managing them.
A.K. and M.L.B. contributed equally to this work.
This article is adapted from a blog piece (Kim A, Hsu M, Koire A, Baum ML. Incidental findings in deep phenotyping research: Legal and ethical considerations. Bill of Health, the Blog of Petrie-Flom Center at Harvard Law School, as Part of the Ethical, Legal, and Social Implications of Deep Phenotyping Symposium; 2021 Feb 10; available at https://blog.petrieflom.law.harvard.edu/2021/02/10/incidental-findings-deep-phenotyping/ [last accessed 1 Mar 2022]).
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