{"id":"W4413157595","doi":"10.1109/icoeca66273.2025.00121","title":"Early Detection of Lung Cancer using DenseNet with AI Support","year":2025,"lang":"en","type":"article","venue":"","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Lung cancer; Artificial intelligence; Medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002480256,0.00009207056,0.0001855152,0.0001294852,0.0003965134,0.000003727402,0.00008564653,0.0001584748,0.0008480171],"category_scores_gemma":[0.00005732061,0.00007347434,0.00002664721,0.0003866761,0.00005550364,0.00009350279,0.00004491748,0.0003657186,0.00002094259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002670067,"about_ca_system_score_gemma":0.001140612,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06463414,"about_ca_topic_score_gemma":0.04717926,"domain_scores_codex":[0.9986958,0.0001624677,0.0004712295,0.0001890137,0.0001517362,0.0003297676],"domain_scores_gemma":[0.9989087,0.0001870857,0.000143108,0.0002210014,0.0004779136,0.00006218119],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001359128,0.00001453808,0.9850966,0.0004851175,0.0000299743,0.000002235442,0.001799828,0.0001934657,0.006364898,0.001215123,0.0005985543,0.004063752],"study_design_scores_gemma":[0.001161584,0.0008097504,0.4477746,0.005530566,0.0004673614,0.000006377179,0.02360942,0.1589771,0.3434851,0.003599172,0.01348536,0.001093565],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9778589,0.00008559279,0.01575018,0.000875632,0.0007414233,0.0006806214,0.000007715825,0.00007015412,0.003929766],"genre_scores_gemma":[0.9946021,0.00001732338,0.0003588526,0.00130289,0.00009266887,0.00007047864,0.000001092158,0.00001401711,0.003540521],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.537322,"threshold_uncertainty_score":0.9702072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1113989197211948,"score_gpt":0.5249210809303554,"score_spread":0.4135221612091606,"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."}}