Computing schizophrenia: ethical challenges for machine learning in psychiatry

Starke, Georg and De Clercq, Eva and Borgwardt, Stefan and Elger, Bernice Simone. (2021) Computing schizophrenia: ethical challenges for machine learning in psychiatry. Psychological medicine, 51 (15). pp. 2515-2521.

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Official URL: https://edoc.unibas.ch/79306/

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Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.
Faculties and Departments:08 Cross-disciplinary Subjects > Ethik > Institut für Bio- und Medizinethik > Bio- und Medizinethik (Elger)
03 Faculty of Medicine > Departement Public Health > Ethik in der Medizin > Bio- und Medizinethik (Elger)
UniBasel Contributors:Starke, Georg and De Clercq, Eva and Borgwardt, Stefan and Elger, Bernice Simone
Item Type:Article, refereed
Article Subtype:Research Article
Publisher:Cambridge University Press
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:18 Nov 2021 15:33
Deposited On:10 Jan 2021 09:28

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