Journal Article

FZJ-2020-04515

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Machine learning for psychiatry: getting doctors at the black box?

Hedderich, D. M. ; Eickhoff, S. (Corresponding author)FZJ*

2021
Macmillan London

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Please use a persistent id in citations: http://hdl.handle.net/2128/26984  doi:10.1038/s41380-020-00931-z

Abstract: Recent developments in the field of machine-learning have spurred high hopes for diagnostic support for psychiatric patients based on brain MRI. But while technical advances are undoubtedly remarkable, the current trajectory of mostly proof-of-concept studies performed on retrospective, often repository-derived data, may not be well suited to yield a substantial impact in clinical practice. Here we review these developments and challenges, arguing for the need of stronger involvement of and input from medical doctors in order to pave the way for machine-learning in clinical psychiatry.

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Contributing Institute(s):

1. Gehirn & Verhalten (INM-7)

Research Program(s):

1. 5252 - Brain Dysfunction and Plasticity (POF4-525) (POF4-525)

Appears in the scientific report 2021

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Database coverage:
; ; ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; Ebsco Academic Search ; Essential Science Indicators ; IF >= 10 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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