Ethical dilemmas posed by mobile health and machine learning in psychiatry research
The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistic...
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| Veröffentlicht in: | Bulletin of the World Health Organization 2020-04, Vol.98 (4), p.270-276 |
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| container_title | Bulletin of the World Health Organization |
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| creator | Jacobson, Nicholas C Bentley, Kate H Walton, Ashley Wang, Shirley B Fortgang, Rebecca G Millner, Alexander J Coombs, 3rd, Garth Rodman, Alexandra M Coppersmith, Daniel D L |
| description | The application of digital technology to psychiatry research is rapidly leading to new discoveries and capabilities in the field of mobile health. However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry. |
| doi_str_mv | 10.2471/BLT.19.237107 |
| format | Article |
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However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. 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However, the increase in opportunities to passively collect vast amounts of detailed information on study participants coupled with advances in statistical techniques that enable machine learning models to process such information has raised novel ethical dilemmas regarding researchers' duties to: (i) monitor adverse events and intervene accordingly; (ii) obtain fully informed, voluntary consent; (iii) protect the privacy of participants; and (iv) increase the transparency of powerful, machine learning models to ensure they can be applied ethically and fairly in psychiatric care. This review highlights emerging ethical challenges and unresolved ethical questions in mobile health research and provides recommendations on how mobile health researchers can address these issues in practice. Ultimately, the hope is that this review will facilitate continued discussion on how to achieve best practice in mobile health research within psychiatry.</description><subject>Algorithms</subject><subject>Best practice</subject><subject>Confidentiality</subject><subject>Consent</subject><subject>Critical incidents</subject><subject>Data collection</subject><subject>Decision making</subject><subject>Ethical dilemmas</subject><subject>Ethics</subject><subject>Ethics, Research</subject><subject>Health research</subject><subject>Information processing</subject><subject>Information seeking behavior</subject><subject>Informed Consent</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Machine Learning - ethics</subject><subject>Medical research</subject><subject>Mental disorders</subject><subject>Mental health</subject><subject>Mental health services</subject><subject>Multiplication</subject><subject>Policy & 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| ispartof | Bulletin of the World Health Organization, 2020-04, Vol.98 (4), p.270-276 |
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| source | MEDLINE; PAIS Index; EZB-FREE-00999 freely available EZB journals; PubMed Central; Sociological Abstracts |
| subjects | Algorithms Best practice Confidentiality Consent Critical incidents Data collection Decision making Ethical dilemmas Ethics Ethics, Research Health research Information processing Information seeking behavior Informed Consent Learning algorithms Machine learning Machine Learning - ethics Medical research Mental disorders Mental health Mental health services Multiplication Policy & Practice Privacy Professional practice Psychiatry Questions Resolvers Smartphones Statistical analysis Technology Telemedicine Telemedicine - ethics Transparency Wrongdoing |
| title | Ethical dilemmas posed by mobile health and machine learning in psychiatry research |
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