{"id":"W4210823616","doi":"10.2196/36895","title":"Interobserver and Human–Artificial Intelligence Concordance in Differentiating Between Invasive and In Situ Melanoma","year":2022,"lang":"en","type":"article","venue":"Iproceedings","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Concordance; Medicine; Melanoma; Artificial intelligence; Diagnostic accuracy; Melanoma diagnosis; Medical physics; Machine learning; Clinical Practice; Computer science; Radiology; Family medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.000211357,0.0001131588,0.0002382763,0.0002270487,0.0001147377,0.00003495369,0.0000620983,0.00003136925,0.00006248584],"category_scores_gemma":[0.00007261797,0.0001209466,0.00002021659,0.0002332547,0.00005070382,0.00007148834,0.0002699446,0.0002889489,0.000002090317],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001166908,"about_ca_system_score_gemma":0.00001191713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002095345,"about_ca_topic_score_gemma":0.0006183616,"domain_scores_codex":[0.9990534,0.00001577256,0.000296905,0.0003008553,0.0001512767,0.0001818096],"domain_scores_gemma":[0.999727,0.00005318981,0.00007750513,0.00005960948,0.00002122471,0.00006151586],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002022295,0.0001106752,0.9008986,0.0002546551,0.000030861,0.0001679192,0.00533697,0.000003057993,0.06855894,0.002259068,0.00007202982,0.02210498],"study_design_scores_gemma":[0.00125403,0.001303526,0.9423732,0.0003421882,0.00007396435,0.0002711897,0.01936196,0.0004960679,0.02808036,0.005016351,0.0009822682,0.000444935],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9979956,0.00007413044,0.00003472149,0.0004469857,0.00007050034,0.0003126217,0.000001811247,0.00003030974,0.001033373],"genre_scores_gemma":[0.9993826,0.00001500707,0.00008291456,0.0002206907,0.00005954454,0.00004501394,0.000005261092,0.00001323696,0.0001757602],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04147454,"threshold_uncertainty_score":0.4932058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03941695790338288,"score_gpt":0.2795349728782815,"score_spread":0.2401180149748986,"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."}}