{"id":"W4294349358","doi":"10.2196/39143","title":"Improving Skin Color Diversity in Cancer Detection: Deep Learning Approach","year":2022,"lang":"en","type":"article","venue":"JMIR Dermatology","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Skin cancer; Deep learning; Convolutional neural network; Artificial intelligence; Dermatology; Medicine; Skin color; Skin lesion; Computer science; Cancer; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00008430031,0.00009384186,0.0002141586,0.0002364986,0.0004309755,0.000007035573,0.00008039804,0.00005773926,0.0005029714],"category_scores_gemma":[0.00001424548,0.0001036447,0.00005632807,0.0002931768,0.00003304292,0.00002989664,0.0004870302,0.0004584224,0.00001579543],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003203795,"about_ca_system_score_gemma":0.00002533502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005068478,"about_ca_topic_score_gemma":0.0003985224,"domain_scores_codex":[0.9990745,0.0001233571,0.0001590288,0.0002507378,0.0001588137,0.000233556],"domain_scores_gemma":[0.9997053,0.0000224017,0.00007563549,0.0001201971,0.00001968155,0.00005682217],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003834699,0.002406521,0.4185856,0.001520085,0.0004846645,0.008445627,0.01683848,0.01663144,0.01814138,0.0008535518,0.003037352,0.5092207],"study_design_scores_gemma":[0.0120299,0.001494859,0.1355522,0.00002644906,0.0002917665,0.03466104,0.02322206,0.3094737,0.005543782,0.00008957096,0.4765494,0.001065194],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.992494,0.0001648954,0.001384647,0.0005310236,0.0003349842,0.0005982706,9.848003e-7,0.0001313199,0.004359887],"genre_scores_gemma":[0.9974188,0.00001209756,0.0001034489,0.0005384253,0.00004208149,0.0004103995,0.000006443931,0.00001215967,0.001456167],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5081555,"threshold_uncertainty_score":0.5507184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585753632189033,"score_gpt":0.2561114650260886,"score_spread":0.2402539287041983,"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."}}