{"id":"W2972112160","doi":"10.1371/journal.pone.0221339","title":"A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis","year":2019,"lang":"en","type":"review","venue":"PLoS ONE","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":185,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; St. Michael's Hospital; McGill University Health Centre","funders":"National Institute of Allergy and Infectious Diseases; McGill University; World Health Organization","keywords":"CAD; Receiver operating characteristic; Medicine; Pulmonary tuberculosis; Medical physics; Diagnostic accuracy; Artificial intelligence; MEDLINE; Tuberculosis; Machine learning; Radiology; Internal medicine; Computer science; Pathology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01886662,0.001715303,0.0105304,0.01396358,0.0006755286,0.00301838,0.001930815,0.00189968,0.002779342],"category_scores_gemma":[0.09383283,0.001357018,0.01116732,0.0117316,0.001074803,0.002079363,0.001424504,0.001073145,0.0003021334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003895702,"about_ca_system_score_gemma":0.009759307,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00810318,"about_ca_topic_score_gemma":0.02518303,"domain_scores_codex":[0.9763264,0.00957189,0.009474966,0.001313549,0.002983409,0.0003299016],"domain_scores_gemma":[0.8880573,0.08812156,0.01415814,0.001330084,0.007770203,0.0005626907],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0003643317,0.00001706776,0.002076775,0.9377462,0.03094316,0.00008894842,0.0001571631,0.0001268005,0.0001709331,0.0001221729,0.0007990622,0.02738743],"study_design_scores_gemma":[0.0005430951,0.0003821021,0.009577831,0.7702646,0.2094338,0.0004144281,0.0001853639,0.0001781855,0.0002494953,0.0002648349,0.008462173,0.00004398799],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.002357213,0.9961879,0.0002631984,0.0002227796,0.0001063273,0.0002898943,0.0003631327,0.000009322245,0.000200379],"genre_scores_gemma":[0.04095772,0.9538752,0.002343599,0.0008759943,0.0001622974,0.001120885,0.0005062058,0.00001468654,0.0001433135],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01886662,"threshold_uncertainty_score":0.0997774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1562271622486037,"score_gpt":0.3627522615328458,"score_spread":0.2065250992842421,"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."}}