{"id":"W4392748966","doi":"10.1148/ryai.230079","title":"Assistive AI in Lung Cancer Screening: A Retrospective Multinational Study in the United States and Japan","year":2024,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Hamilton Health Sciences; Google","keywords":"Medicine; Retrospective cohort study; Receiver operating characteristic; Lung cancer; Workflow; Medical physics; Lung cancer screening; Multinational corporation; Artificial intelligence; General surgery; Surgery; Pathology; Internal medicine; Computer science; Database","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005409506,0.0006495044,0.0006778124,0.00165954,0.001091129,0.001608192,0.0009748135,0.0007952247,0.001052752],"category_scores_gemma":[0.008806088,0.000966704,0.0009880456,0.00252937,0.001246978,0.001289994,0.00154553,0.0006515634,0.0002972157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001312353,"about_ca_system_score_gemma":0.0009782424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02872727,"about_ca_topic_score_gemma":0.03014936,"domain_scores_codex":[0.9961868,0.001546134,0.0005365617,0.0008513468,0.0005077604,0.0003714195],"domain_scores_gemma":[0.9890651,0.002058711,0.004773384,0.001421403,0.001386736,0.001294695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007057768,0.00004897751,0.9990481,0.000008733488,0.00004702635,0.00004604172,0.0002841092,0.00001018244,0.00005691448,0.000004365947,0.00003241219,0.0003426381],"study_design_scores_gemma":[0.00001399433,0.0002054674,0.9978289,0.00001115013,0.0000822848,0.0001318992,0.001379695,0.000154032,0.00005161257,0.000006972876,0.0001263083,0.000007664139],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9995279,0.0001577368,0.00004770361,0.00002657593,0.000003061003,0.0000168565,0.00008011518,0.000001571777,0.0001382885],"genre_scores_gemma":[0.9994323,0.0001154153,0.00008791547,0.00008449122,0.000008681324,0.00002427073,0.0001706605,0.000004106445,0.00007224808],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02872727,"threshold_uncertainty_score":0.05712008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04008904032462744,"score_gpt":0.384285339188543,"score_spread":0.3441962988639155,"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."}}