{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04343095,0.0005476821,0.0006007496,0.00438075,0.0008169973,0.002711193,0.001208118,0.001309902,0.001663982],"category_scores_gemma":[0.1141599,0.0004438233,0.001066646,0.001339649,0.001843259,0.001584785,0.002807396,0.0007683958,0.0005690641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007706562,"about_ca_system_score_gemma":0.0004557534,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001882514,"about_ca_topic_score_gemma":0.003892472,"domain_scores_codex":[0.9587076,0.02162532,0.005738314,0.007267522,0.005548361,0.001112864],"domain_scores_gemma":[0.8198268,0.1271545,0.01412449,0.01987036,0.01716651,0.001857368],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001427974,0.00009077366,0.9315631,0.000489686,0.001247074,0.0003769061,0.002877111,0.004187444,0.003369243,0.0008754993,0.00174296,0.05175229],"study_design_scores_gemma":[0.00005385433,0.0003215601,0.936379,0.0003128522,0.000924707,0.002680011,0.00216166,0.03259677,0.01215833,0.004970116,0.007269265,0.0001719735],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9499099,0.003481178,0.02715755,0.0003572771,0.0004987313,0.0002660305,0.0009603406,0.0003747565,0.01699426],"genre_scores_gemma":[0.9940661,0.0002177516,0.004675337,0.0000824141,0.00007900444,0.00005243687,0.0003944092,0.00005949554,0.0003729289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04343095,"threshold_uncertainty_score":0.2296875,"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."}}