Incidental Findings from Deep Phenotyping Research in Psychiatry: Legal and Ethical Considerations
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 mach...
| Main Author: | |
|---|---|
| Contributors: | ; ; |
| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
|
| Interlibrary Loan: | Interlibrary Loan for the Fachinformationsdienste (Specialized Information Services in Germany) |
| Published: |
2022
|
| In: |
Cambridge quarterly of healthcare ethics
Year: 2022, Volume: 31, Issue: 4, Pages: 482-486 |
| Further subjects: | B
Ethics
B incidental findings B smart phones B Machine Learning B Psychiatry B deep phenotyping |
| Online Access: |
Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
| Summary: | 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. |
|---|---|
| ISSN: | 1469-2147 |
| Contains: | Enthalten in: Cambridge quarterly of healthcare ethics
|
| Persistent identifiers: | DOI: 10.1017/S0963180122000135 |