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Journal of clinical nursing
Feb 24, 2025;

Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta-Analysis.

Yee Shyan Goh, Qi Rui See, Nopporn Vongsirimas, Piyanee Klanin-Yobas
Author Information
  1. Yee Shyan Goh: Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore, Singapore.
  2. Qi Rui See: Department of Biological Sciences, School of Biological Sciences, Nanyang Technological University, Singapore, Singapore.
  3. Nopporn Vongsirimas: Faculty of Nursing, Mahidol University, Salaya, Nakhon Pathom, Thailand.
  4. Piyanee Klanin-Yobas: Alice Lee Centre for Nursing Studies, National University of Singapore, Singapore, Singapore. ORCID
PMID: 39994873 DOI: 10.1111/jocn.17694

Abstract

AIM: To synthesise existing evidence concerning the application of AI methods in detecting depression through behavioural cues among adults in healthcare and community settings.
DESIGN: This is a diagnostic accuracy systematic review.
METHODS: This review included studies examining different AI methods in detecting depression among adults. Two independent reviewers screened, appraised and extracted data. Data were analysed by meta-analysis, narrative synthesis and subgroup analysis.
DATA SOURCES: Published studies and grey literature were sought in 11 electronic databases. Hand search was conducted on reference lists and two journals.
RESULTS: In total, 30 studies were included in this review. Twenty of which demonstrated that AI models had the potential to detect depression. Speech and facial expression showed better sensitivity, reflecting the ability to detect people with depression. Text and movement had better specificity, indicating the ability to rule out non-depressed individuals. Heterogeneity was initially high. Less heterogeneity was observed within each modality subgroup.
CONCLUSIONS: This is the first systematic review examining AI models in detecting depression using all four behavioural cues: speech, texts, movement and facial expressions.
IMPLICATIONS: A collaborative effort among healthcare professionals can be initiated to develop an AI-assisted depression detection system in general healthcare or community settings.
IMPACT: It is challenging for general healthcare professionals to detect depressive symptoms among people in non-psychiatric settings. Our findings suggested the need for objective screening tools, such as an AI-assisted system, for screening depression. Therefore, people could receive accurate diagnosis and proper treatments for depression.
REPORTING METHOD: This review followed the PRISMA checklist.
PATIENTS OR PUBLIC CONTRIBUTION: No patients or public contribution.

Keywords

artificial intelligence facial expressions machine learning movement speech text

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Created with Highcharts 10.0.0depressionreviewAIamonghealthcaredetectingsettingsstudiesdetectfacialpeoplemovementmethodsbehaviouraladultscommunitysystematicincludedexaminingsubgroupmodelsbetterabilityspeechexpressionsprofessionalsAI-assistedsystemgeneralscreeningAIM:synthesiseexistingevidenceconcerningapplicationcuesDESIGN:diagnosticaccuracyMETHODS:differentTwoindependentreviewersscreenedappraisedextracteddataDataanalysedmeta-analysisnarrativesynthesisanalysisDATASOURCES:Publishedgreyliteraturesought11electronicdatabasesHandsearchconductedreferenceliststwojournalsRESULTS:total30TwentydemonstratedpotentialSpeechexpressionshowedsensitivityreflectingTextspecificityindicatingrulenon-depressedindividualsHeterogeneityinitiallyhighLessheterogeneityobservedwithinmodalityCONCLUSIONS:firstusingfourcues:textsIMPLICATIONS:collaborativeeffortcaninitiateddevelopdetectionIMPACT:challengingdepressivesymptomsnon-psychiatricfindingssuggestedneedobjectivetoolsThereforereceiveaccuratediagnosispropertreatmentsREPORTINGMETHOD:followedPRISMAchecklistPATIENTSORPUBLICCONTRIBUTION:patientspubliccontributionArtificialIntelligenceDiagnosingDepressionBehaviouralCues:DiagnosticAccuracySystematicReviewMeta-Analysisartificialintelligencemachinelearningtext

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