biostudies-literatureUnknown21(1)Chen Z<h4>Background</h4>The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.<h4>Methods</h4>Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychBMC medicine241https://www.ebi.ac.uk/biostudies/studies/S-EPMC10318841biostudies-literatureSampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.PMC10318841Chuan-Peng HGu XLi CFeng ZChen JXiao ZEickhoff SBHu BMiao KDai XLiu XChen ZLeonov ABecker BTang YfalseSampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.<h4>Background</h4>The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation.<h4>Methods</h4>Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psych2023-01-01T00:00:00Z2023 Jul2025-04-26T18:44:46.11Z2025-04-06T15:53:10.179ZS-EPMC103188413740081410.1186/s12916-023-02941-4