Machine learning for psychiatry: getting doctors at the black box?

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 reposi...

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Detalles Bibliográficos
Autores principales: Hedderich, Dennis M., Eickhoff, Simon B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815505/
https://www.ncbi.nlm.nih.gov/pubmed/33173196
http://dx.doi.org/10.1038/s41380-020-00931-z
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author Hedderich, Dennis M.
Eickhoff, Simon B.
author_facet Hedderich, Dennis M.
Eickhoff, Simon B.
author_sort Hedderich, Dennis M.
collection PubMed
description 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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spelling pubmed-78155052021-01-25 Machine learning for psychiatry: getting doctors at the black box? Hedderich, Dennis M. Eickhoff, Simon B. Mol Psychiatry Comment 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. Nature Publishing Group UK 2020-11-10 2021 /pmc/articles/PMC7815505/ /pubmed/33173196 http://dx.doi.org/10.1038/s41380-020-00931-z Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Comment
Hedderich, Dennis M.
Eickhoff, Simon B.
Machine learning for psychiatry: getting doctors at the black box?
title Machine learning for psychiatry: getting doctors at the black box?
title_full Machine learning for psychiatry: getting doctors at the black box?
title_fullStr Machine learning for psychiatry: getting doctors at the black box?
title_full_unstemmed Machine learning for psychiatry: getting doctors at the black box?
title_short Machine learning for psychiatry: getting doctors at the black box?
title_sort machine learning for psychiatry: getting doctors at the black box?
topic Comment
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7815505/
https://www.ncbi.nlm.nih.gov/pubmed/33173196
http://dx.doi.org/10.1038/s41380-020-00931-z
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