{"id":"W4399455542","doi":"10.2196/48811","title":"Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis","year":2024,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Cross-sectional study; Sunscreening Agents; Dermatology; Medicine; Computer science; World Wide Web; Pathology; Skin cancer","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":[0.0001293486,0.0001538645,0.0004707343,0.0008883766,0.00004223009,0.00004588826,0.000119162,0.0002172448,0.0006839538],"category_scores_gemma":[0.00007801182,0.0001367356,0.0002347051,0.001170289,0.000184263,0.00007449198,0.00005864475,0.000275838,0.0001385908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006298847,"about_ca_system_score_gemma":0.00004982408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003409306,"about_ca_topic_score_gemma":0.0001466781,"domain_scores_codex":[0.9983259,0.000100328,0.0006581307,0.0004292126,0.0002181704,0.0002682404],"domain_scores_gemma":[0.9992794,0.0002164866,0.00006635053,0.0002719305,0.00005201872,0.0001137822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008722498,0.001753802,0.941305,0.0005078078,0.0009507999,0.007256399,0.0006755033,0.001239163,0.0008428639,0.01717515,0.0003686878,0.02705252],"study_design_scores_gemma":[0.0004632124,0.0002613705,0.9344783,0.00006163309,0.0004104156,0.007413518,0.000272704,0.03620514,0.01088376,0.001713732,0.007538354,0.0002978449],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948162,0.0002847649,0.002463108,0.0007449088,0.00033149,0.000427598,0.000005984529,0.0002057932,0.0007201204],"genre_scores_gemma":[0.9990975,0.00003853623,0.0002623924,0.0002158379,0.00006789987,0.0001890216,0.0000416658,0.00001352945,0.00007365167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03496597,"threshold_uncertainty_score":0.7488815,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03916212260616856,"score_gpt":0.3648515988482846,"score_spread":0.325689476242116,"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."}}