% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Hedderich:887913,
      author       = {Hedderich, Dennis M. and Eickhoff, Simon},
      title        = {{M}achine learning for psychiatry: getting doctors at the
                      black box?},
      journal      = {Molecular psychiatry},
      volume       = {26},
      issn         = {1476-5578},
      address      = {London},
      publisher    = {Macmillan},
      reportid     = {FZJ-2020-04515},
      pages        = {23-25},
      year         = {2021},
      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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33173196},
      UT           = {WOS:000588233100003},
      doi          = {10.1038/s41380-020-00931-z},
      url          = {https://juser.fz-juelich.de/record/887913},
}