{"id":"W4379466703","doi":"10.12788/cutis.0764","title":"Artificial Intelligence vs Medical Providers in the Dermoscopic Diagnosis of Melanoma","year":2023,"lang":"en","type":"article","venue":"Cutis","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Engineers Without Borders Canada","funders":"","keywords":"Medicine; Melanoma; Teledermatology; Referral; Dermatology; Melanoma diagnosis; Biopsy; Predictive value; Diagnostic accuracy; Pathology; Radiology; Internal medicine; Health care; Telemedicine; Family 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007896399,0.0002641434,0.0002678631,0.001551517,0.000452015,0.001261565,0.0003071735,0.0008231688,0.001890413],"category_scores_gemma":[0.04074207,0.0002234441,0.0002158403,0.0007851674,0.0006633871,0.001264002,0.0007850321,0.0007593101,0.0004681147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005017206,"about_ca_system_score_gemma":0.0006940071,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002063053,"about_ca_topic_score_gemma":0.002597142,"domain_scores_codex":[0.9940302,0.003972476,0.0003520775,0.0003086693,0.001039147,0.0002975439],"domain_scores_gemma":[0.9711338,0.0215541,0.003452271,0.000687864,0.002211648,0.0009602942],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001720173,0.0005508649,0.9095529,0.0002025657,0.00008231603,0.0006607917,0.001483779,0.001344263,0.001603801,0.0008646928,0.0009963941,0.08093745],"study_design_scores_gemma":[0.0004485674,0.004187525,0.9025888,0.0007842017,0.0003370845,0.007842641,0.006233681,0.05723108,0.006297107,0.006275118,0.007693474,0.00008066868],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9802731,0.001844727,0.003839379,0.003836743,0.0001229245,0.0002148636,0.0001208761,0.00005002076,0.009697274],"genre_scores_gemma":[0.9956062,0.0003313895,0.003192915,0.000502163,0.00007282564,0.00003498768,0.00002254756,0.000003425512,0.0002334935],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007896399,"threshold_uncertainty_score":0.04176062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03917866017792736,"score_gpt":0.3087719468349236,"score_spread":0.2695932866569962,"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."}}