Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts
Abstract: The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Much recent literature examines the ethical consequences of employing ML within medical and psychiatric practice but the potential impact on psychiatric diagnostic systems has so far not been well-developed. In this article, we aim to explore the challenges that arise from the recent use of ANNs for the old problems of psychiatric nosology. To enable an empirically supported critical reflection on the topic, we conducted semi-structured qualitative interviews with Swiss and German experts in computational psychiatry. Here, we report our findings structured around two themes, namely (1) the possibility of using ML for defining or refining of psychiatric classification, and (2) the desirability of employing ML for psychiatric nosology.... https://philosophymindscience.org/index.php/phimisci/article/view/9435
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
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Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts ; volume:4 ; year:2023
Philosophy and the mind sciences ; 4 (2023)
- Creator
- DOI
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10.33735/phimisci.2023.9435
- URN
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urn:nbn:de:101:1-2023041918544062534554
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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14.08.2025, 10:46 AM CEST
Data provider
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Starke, Georg
- Elger, Bernice S.
- De Clercq, Eva
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