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. We discuss these themes by relating them to recent debates about network theory for psychiatric nosology and show why empirical research in the field should critically reflect on its contribution to psychopathology research. In sum, we argue that beyond technical, regulatory, and ethical challenges, philosophical reflection is crucial to harness the potential of ML in psychiatry.

Out of their minds? Externalist challenges for using AI in forensic psychiatry

“…Identifying individual clinical phenotypes in bipolar disorders ( 4 ), predicting psychotic episodes in at-risk patients ( 5 ), managing mood disorders through using digital phenotyping ( 6 ) or selecting the most suitable psychopharmacological intervention in depression or schizophrenia ( 7–9 ) can seemingly all be improved by harnessing the computational power of ML for large-scale datasets. Ultimately, even the very classification of psychiatric disorders may be overhauled or at least refined by drawing on results from AI-based research ( 10–12 ).…”