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| Hauptseite > Publikationsdatenbank > Machine learning for psychiatry: getting doctors at the black box? > print |
| Hauptseite > Publikationsdatenbank > Machine learning for psychiatry: getting doctors at the black box? > print |
| 001 | 887913 | ||
| 005 | 20211028141343.0 | ||
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| 037 | _ | _ | |a FZJ-2020-04515 |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Hedderich, Dennis M. |0 0000-0001-8994-5593 |b 0 |
| 245 | _ | _ | |a Machine learning for psychiatry: getting doctors at the black box? |
| 260 | _ | _ | |a London |c 2021 |b Macmillan |
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| 520 | _ | _ | |a 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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| 700 | 1 | _ | |a Eickhoff, Simon |0 P:(DE-Juel1)131678 |b 1 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1038/s41380-020-00931-z |0 PERI:(DE-600)1502531-7 |p 23-25 |t Molecular psychiatry |v 26 |y 2021 |x 1476-5578 |
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