Computing schizophrenia: ethical challenges for machine learning in psychiatry
Open accessAbstract
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.
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Obtaining consent may be difficult for black box algorithms that are too opaque to be fully understood by humans.
“Obtaining consent may be difficult for black box algorithms that are too opaque to be fully understood by humans [33]”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [33]
Training data may reflect systemic biases based on gender, race, and other sociodemographic characteristics-for instance, the disproportionate number of African-Americans who suffer from schizophrenia.
“training data may also reflect systemic biases based on gender, race, and other sociodemographic characteristics-for instance, the disproportionate number of African-Americans who suffer from schizophrenia [33].”
From Operationalising ethics in artificial intelligence for healthcare: a framework for AI developers (2023) · cites this paper as [33]
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Background
- Authors
- Starke, G., De Clercq, E., Borgwardt, S., & Elger, B. S.
- Affiliations
- University of Basel (CH)
- University of Geneva (CH)
- Corresponding author
- Starke, Georg
- Venue
- Psychological Medicine
- Publisher
- Cambridge University Press (CUP)
- Published
- 2020-06-15
- Volume
- 51
- Issue
- 15
- Pages
- 2515-2521
- ISSN
- 0033-2917 (ISSN-L)
- 1469-8978
- OpenAlex
- W3034468175
- Cited by
- 62
- References
- 82
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 11 · 2023: 7 · 2022: 14 · 2021: 17 · 2020: 3
- FWCI
- 3.06
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Psychological Medicine (publishedVersion)
- PubMed (publishedVersion)
- edoc (University of Basel) (submittedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neuroethics, Human Enhancement, Biomedical Innovations
- Keywords
- schizophrenia, AI ethics, principlism, beneficence, non-maleficence, respect for autonomy, justice, psychiatric diagnosis, treatment prediction, clinical implementation
- MeSH terms
- Machine Learning, Algorithms, Bioethics, Diagnosis, Computer-Assisted, Humans, Psychiatry, Schizophrenia
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