Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review
Open accessAbstract
Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.
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Of the 555 neuroimaging AI models in Chen et al., only 86 studies (15.5%) included external validation.
“identifying only 86 studies (15.5%) included external validation”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
Overall, 83% of studies in Chen et al. were rated at high risk of bias.
“Overall, 83% of studies were rated at high ROB18.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
Chen et al. examined 555 published neuroimaging-based AI models for psychiatric diagnosis.
“examined 555 published neuroimaging-based AI models for psychiatric diagnosis”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
In Chen et al., 97.5% of the studies included only subjects from high-income regions.
“while 97.5% included only subjects from high-income regions.”
From Bias recognition and mitigation strategies in artificial intelligence healthcare applications (2025) · cites this paper as [18]
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Chyzhyk, D., Savio, A. M., & Graña, M. (2015) · Neural Networks · doi:10.1016/j.neunet.2015.04.002 · Cited by 93 · see citing works
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Nicholson, A. A., Harricharan, S., Densmore, M., Neufeld, R. W. J., Ros, T., McKinnon, M. C., Frewen, P., Théberge, J., Jetly, R., Pedlar, D., et al. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102262 · Cited by 92 · see citing works
Identifying major depressive disorder using Hurst exponent of resting-state brain networks
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Aydın, S., Arıca, N., Ergül, E., & Tan, O. (2015) · International Journal of Neural Systems · doi:10.1142/s0129065715500100 · Cited by 89 · see citing works
Wee, C.-Y., Wang, L., Shi, F., Yap, P.-T., & Shen, D. (2014) · Human Brain Mapping · doi:10.1002/hbm.22411 · Cited by 89 · see citing works
Mueller, A., Candrian, G., Grane, V. A., Kropotov, J. D., Ponomarev, V. A., & Baschera, G.-M. (2011) · Nonlinear Biomedical Physics · doi:10.1186/1753-4631-5-5 · Cited by 88 · see citing works
Bürger, C., Redlich, R., Grotegerd, D., Meinert, S. L., Dohm, K., Schneider, I., Zaremba, D., Förster, K., Alferink, J., Bölte, J., et al. (2017) · Neuropsychopharmacology · doi:10.1038/npp.2017.36 · Cited by 88 · see citing works
Cabral, C., Kambeitz‐Ilankovic, L., Kambeitz, J. P., Calhoun, V. D., Dwyer, D., von Saldern, S., Urquijo, M. F., Falkai, P., & Koutsouleris, N. (2016) · Schizophrenia Bulletin · doi:10.1093/schbul/sbw053 · Cited by 88 · see citing works
Evaluating risk prediction models for adults with heart failure: A systematic literature review
Tanna, G. L. D., Wirtz, H. S., Burrows, K. L., & Globe, G. A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0224135 · Cited by 88 · see citing works
Classification of ASD based on fMRI data with deep learning
Shao, L., Fu, C., You, Y., & Fu, D. (2021) · Cognitive Neurodynamics · doi:10.1007/s11571-021-09683-0 · Cited by 88 · see citing works
Yang, C., Wang, P., Tan, J. H., Liu, Q., & Li, X. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104963 · Cited by 88 · see citing works
Kernel Principal Component Analysis for dimensionality reduction in fMRI-based diagnosis of ADHD
Sidhu, G. S., Asgarian, N., Greiner, R., & Brown, M. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00074 · Cited by 87 · see citing works
Flagged first, then by citations
42 of the 614 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
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Data leakage inflates prediction performance in connectome-based machine learning models
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BrainAGE, brain health, and mental disorders: A systematic review
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Updated Perspectives on the Neurobiology of Substance Use Disorders Using Neuroimaging
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A new era in cognitive neuroscience: the tidal wave of artificial intelligence (AI)
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The Transition From Homogeneous to Heterogeneous Machine Learning in Neuropsychiatric Research
Zhao, Q., Nooner, K. B., Tapert, S. F., Adeli, E., Pohl, K. M., Kuceyeski, A. F., & Sabuncu, M. R. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100397 · Cited by 11 · see citing works
AI Integration in Behavioral Healthcare: A Practical Framework for Clinicians
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Applications of MRI in Schizophrenia: Current Progress in Establishing Clinical Utility
Sun, H. J., Liu, N., Qiu, C., Tao, B., Yang, C., Tang, B., Li, H., Zhan, K., Cai, C., Zhang, W., et al. (2024) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.29470 · Cited by 4 · see citing works
Integrative diagnosis of psychiatric conditions using ChatGPT and fMRI data
Li, R. (2025) · BMC Psychiatry · doi:10.1186/s12888-025-06586-w · Cited by 4 · see citing works
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Ibrahim, Z. A., & Al-Sambawee, M. A. J. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-00956-3 · Cited by 3 · see citing works
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Are AI Neuroimaging Models Ready for Clinical Use? A Systematic Methodological Review
Sulaimanov, U., Şanlıer, N., Moniri, A., Demir, B., Serikkanov, Y. S., BAYRAMOĞLU, A. R., Al-Jebur, M. S., Uslu, I., Ozturk, O., Nizzola, M., et al. (2026) · Journal of Clinical Medicine · doi:10.3390/jcm15093441 · Cited by 4 · see citing works
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Vitacco, M. J., Nelson, R. J., Staats, M. L. P., & Coleman, S. (2024) · Forensic Science International Mind and Law · doi:10.1016/j.fsiml.2024.100131 · Cited by 3 · see citing works
A scoping review of magnetic resonance angiography and perfusion image synthesis
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Artificial intelligence in insanity evaluation. Potential opportunities and current challenges
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Swethaa, T., Manjushri, M., Lekha Chandhini, A., G, J. V., Saranya, G., & Padmanabha Sarma, A. (2025) · DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry (IDICAIHEI) · doi:10.1109/idicaihei65991.2025.11379865
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- Authors
- Chen, Z., Liu, X., Yang, Q., Wang, Y.-J., Miao, K., Gong, Z., Yu, Y., Leonov, A., Liu, C., Feng, Z., & Chuan-Peng, H.
- Affiliations
- Army Medical University (CN)
- Clark University (US)
- Qufu Normal University (CN)
- Nanjing Normal University (CN)
- Corresponding author
- Chen, Zhiyi
- Feng, Zhengzhi
- ORCID
- Venue
- JAMA Network Open
- Publisher
- American Medical Association (AMA)
- Published
- 2023-03-06
- Volume
- 6
- Issue
- 3
- Pages
- e231671
- ISSN
- 2574-3805 (ISSN-L)
- PMC
- PMC9989906
- OpenAlex
- W4323295479
- Cited by
- 43
- References
- 614
- Citations by year
- 2026: 20 · 2025: 10 · 2024: 10 · 2023: 3
- FWCI
- 0.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- JAMA Network Open (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Clark Digital Commons (Clark University) (submittedVersion, cc-by-nc-nd)
- Funders
- Chinese People’s Liberation Army
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- psychiatric diagnosis, reporting quality, PROBAST, clinical applicability, model validation, sample size adequacy, model calibration
- MeSH terms
- Artificial Intelligence, Calibration, Humans, Bias, Benchmarking, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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