{"id":"W4414686796","doi":"10.2196/79293","title":"AI Applications in Depression Detection and Diagnosis: Bibliometric and Visual Analysis of Trends and Future Directions","year":2025,"lang":"en","type":"article","venue":"JMIR Mental Health","topic":"Mental Health via Writing","field":"Psychology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Depression (economics); Mental health; Focus (optics); Visual methods; MEDLINE","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01874491,0.000940472,0.002000368,0.2148969,0.001156936,0.007598063,0.001257673,0.0008623308,0.005392733],"category_scores_gemma":[0.08648485,0.0003916342,0.002445274,0.2147031,0.001126336,0.00643928,0.002916353,0.0008225325,0.00116379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003110228,"about_ca_system_score_gemma":0.005091486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006023453,"about_ca_topic_score_gemma":0.01021682,"domain_scores_codex":[0.9832614,0.003724382,0.004752674,0.001587198,0.006153781,0.0005206106],"domain_scores_gemma":[0.873808,0.07621938,0.02310244,0.003412622,0.02195283,0.001504805],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004057648,0.0001111134,0.27068,0.09374112,0.004236493,0.0006285903,0.005833445,0.002039894,0.001885231,0.013597,0.05860825,0.5482331],"study_design_scores_gemma":[0.0001448248,0.0003385509,0.5392596,0.05300916,0.008062848,0.002363623,0.01376698,0.009597423,0.002796905,0.03091332,0.3394073,0.0003395381],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.2156218,0.5230331,0.01473734,0.01896894,0.001384065,0.001552221,0.1666504,0.001612354,0.05643975],"genre_scores_gemma":[0.6870108,0.2210934,0.03354815,0.001373106,0.001790382,0.001667131,0.04967808,0.0002713795,0.003567569],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9812551,"threshold_uncertainty_score":0.09913379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.017055974860612,"score_gpt":0.4414066292989479,"score_spread":0.4243506544383359,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}