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Incidental Findings from Deep Phenotyping Research in Psychiatry: Legal and Ethical Considerations.

Amanda KimMichael HsuAmanda KoireMatthew L Baum
Published in: Cambridge quarterly of healthcare ethics : CQ : the international journal of healthcare ethics committees (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.
Keyphrases
  • high throughput
  • machine learning
  • big data
  • public health
  • artificial intelligence
  • decision making
  • rare case
  • data analysis