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Cited by 43Artificial Intelligence in Healthcare and EducationMeta-analysis and systematic reviewsEthics and Social Impacts of AI

The DOI matches its Crossref record; 571 of the 614 references it cites were found in a registry or an index, and 1 are not checked yet.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review

Open access
Chen, Zhiyi, Liu, Xuerong, Yang, Qingwu, Wang, Yan-Jiang, Miao, Kuan, Gong, Zheng, Yu, Yang, Leonov, Artemiy, Liu, Chunlei, Feng, Zhengzhi, Chuan-Peng, Hu·2023·JAMA Network Open
DOI 10.1001/jamanetworkopen.2023.1671PMC PMC9989906
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Abstract

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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Crossref · 10.1001/jamanetworkopen.2023.1671

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Figure 1. Evidence Review Pipeline Based on PRISMA 2020 Guideline DSM indicates Diagnostic and Statistical Manual of Mental Disorders. aOf the 555 artificial intelligence (AI) models, 469 were tested at the internal sample and 86 at the independent external sample, including intellectual disability,25,26,27,28 autism spectrum disorder,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147 attention-deficit/hyperactivity disorder,44,51,72,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381 bipolar disorder,290,314,321,327,328,339,343,346,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406 major depressive disorder,309,346,359,363,381,405,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473 social anxiety disorder,474,475,476,477 unspecified anxiety disorder,346,478,479 general anxiety disorder,480 obsessive-compulsive disorders,346,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496 posttraumatic stress disorder,346,409,497,498,499,500,501,502,503 somatic symptom disorder,504 anorexia nervosa,505,506,507 binge-eating disorder,508 insomnia disorder,509,510 conduct disorder,511,512,513 antisocial personality disorder,514,515,516 other or unknown substance use disorder,517,518,519,520 alcohol use disorder,346,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536 opioid use disorder,537 borderline personality disorder,538,539,540 disruptive behavior disorders,541 and social anxiety disorder.346 bEighty-six AI models30,32,34,36,42,44,46,53,59,60,61,63,69,70,72,81,82,88,91,117,149,151,156,162,163,164,169,170,172,180,182,187,191,197,212,213,220,221,247,254,258,260,261,263,269,272,278,280,283,284,285,287,290,301,305,307,319,335,352,353,355,356,359,385,407,417,422,443,452,454,483,484,510,519,526,532,534 were tested at the independently external sample.
Figure 1. Evidence Review Pipeline Based on PRISMA 2020 Guideline DSM indicates Diagnostic and Statistical Manual of Mental Disorders. aOf the 555 artificial intelligence (AI) models, 469 were tested at the internal sample and 86 at the independent external sample, including intellectual disability,25,26,27,28 autism spectrum disorder,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147 attention-deficit/hyperactivity disorder,44,51,72,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,238,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381 bipolar disorder,290,314,321,327,328,339,343,346,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404,405,406 major depressive disorder,309,346,359,363,381,405,407,408,409,410,411,412,413,414,415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431,432,433,434,435,436,437,438,439,440,441,442,443,444,445,446,447,448,449,450,451,452,453,454,455,456,457,458,459,460,461,462,463,464,465,466,467,468,469,470,471,472,473 social anxiety disorder,474,475,476,477 unspecified anxiety disorder,346,478,479 general anxiety disorder,480 obsessive-compulsive disorders,346,481,482,483,484,485,486,487,488,489,490,491,492,493,494,495,496 posttraumatic stress disorder,346,409,497,498,499,500,501,502,503 somatic symptom disorder,504 anorexia nervosa,505,506,507 binge-eating disorder,508 insomnia disorder,509,510 conduct disorder,511,512,513 antisocial personality disorder,514,515,516 other or unknown substance use disorder,517,518,519,520 alcohol use disorder,346,521,522,523,524,525,526,527,528,529,530,531,532,533,534,535,536 opioid use disorder,537 borderline personality disorder,538,539,540 disruptive behavior disorders,541 and social anxiety disorder.346 bEighty-six AI models30,32,34,36,42,44,46,53,59,60,61,63,69,70,72,81,82,88,91,117,149,151,156,162,163,164,169,170,172,180,182,187,191,197,212,213,220,221,247,254,258,260,261,263,269,272,278,280,283,284,285,287,290,301,305,307,319,335,352,353,355,356,359,385,407,417,422,443,452,454,483,484,510,519,526,532,534 were tested at the independently external sample.CC BY-NC-ND · PubMed Central
Figure 2. Geospatial Model for Sample Population Total sample sizes for countries were geologically mapped into corresponding location in the global map. To improve readability, original total sample size underwent log-transformation.
Figure 2. Geospatial Model for Sample Population Total sample sizes for countries were geologically mapped into corresponding location in the global map. To improve readability, original total sample size underwent log-transformation.CC BY-NC-ND · PubMed Central

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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)
DOI
10.1001/jamanetworkopen.2023.1671
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
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
  • Physical Sciences › Computer Science › Artificial Intelligence › 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

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