TY - JOUR
T1 - Computing schizophrenia
T2 - Ethical challenges for machine learning in psychiatry
AU - Starke, Georg
AU - De Clercq, Eva
AU - Borgwardt, Stefan
AU - Elger, Bernice Simone
N1 - Publisher Copyright:
Copyright © The Author(s), 2020. Published by Cambridge University Press.
PY - 2021/11/15
Y1 - 2021/11/15
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85089458824&partnerID=8YFLogxK
U2 - 10.1017/S0033291720001683
DO - 10.1017/S0033291720001683
M3 - Scientific review articles
C2 - 32536358
AN - SCOPUS:85089458824
SN - 0033-2917
VL - 51
SP - 2515
EP - 2521
JO - Psychological Medicine
JF - Psychological Medicine
IS - 15
ER -