{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00046052,0.0001303306,0.0002276311,0.0002792673,0.00006320366,0.00003340655,0.00007005846,0.00006390347,0.00008189469],"category_scores_gemma":[0.0001166809,0.00008759891,0.00003154147,0.0006467603,0.0001609742,0.00005478991,0.00002434817,0.0003453681,0.000005503176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003635038,"about_ca_system_score_gemma":0.00008312828,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003434381,"about_ca_topic_score_gemma":0.005607856,"domain_scores_codex":[0.9988323,0.0001779467,0.0002727747,0.0003747044,0.0001405224,0.0002017258],"domain_scores_gemma":[0.9992533,0.0004961404,0.0000304317,0.0001165411,0.00006692638,0.00003671311],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001968571,0.000508478,0.9709004,0.00003024596,0.0001700471,0.0002699696,0.0115286,0.0008218511,0.00002695056,0.005779813,0.0003371438,0.009429655],"study_design_scores_gemma":[0.0001565639,0.0004581622,0.9212683,0.0001922712,0.00009057991,0.00002847187,0.004526658,0.07172292,0.0001859623,0.001153431,0.0001258445,0.00009078305],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9887173,0.001830446,0.0007190653,0.007631358,0.0001622298,0.0008213091,0.000018245,0.00002544721,0.00007455634],"genre_scores_gemma":[0.9983549,0.0004524313,0.00006336762,0.0005862287,0.0001036683,0.0003725525,0.000026384,0.00000954729,0.00003096606],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07090107,"threshold_uncertainty_score":0.519178,"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."}}