{"id":"W4391528827","doi":"10.1038/s41591-023-02728-3","title":"Deep learning-aided decision support for diagnosis of skin disease across skin tones","year":2024,"lang":"en","type":"article","venue":"Nature Medicine","topic":"Cutaneous Melanoma Detection and Management","field":"Medicine","cited_by":135,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Medical diagnosis; Teledermatology; Diagnostic accuracy; Board certification; Artificial intelligence; Certification; Medicine; Medical decision making; Machine learning; Medical imaging; Medical physics; Continuing medical education; Computer science; Family medicine; Medical education; Health care; Radiology; Continuing education; Telemedicine","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":[0.002309983,0.0005294714,0.0004330586,0.0004972497,0.000368652,0.0006574666,0.0006918793,0.0009001511,0.002371902],"category_scores_gemma":[0.01147055,0.0002137828,0.0003711483,0.0003688067,0.0002939358,0.0006824876,0.0008841041,0.001184602,0.0004798232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008716717,"about_ca_system_score_gemma":0.001185313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007523705,"about_ca_topic_score_gemma":0.008898262,"domain_scores_codex":[0.9988212,0.0005887969,0.00007213213,0.0002557369,0.0001570188,0.0001050919],"domain_scores_gemma":[0.9943528,0.004437188,0.0002763181,0.0002531349,0.0004463384,0.000234317],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003918892,0.002400939,0.07652992,0.0003851116,0.0003678497,0.0006014105,0.0006952151,0.2846009,0.01461807,0.002327021,0.01193266,0.6016219],"study_design_scores_gemma":[0.0001392041,0.0004350018,0.007472848,0.00003657478,0.00005626606,0.0001507868,0.0001277469,0.9791132,0.0067232,0.003863483,0.001859245,0.0000224728],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8481656,0.001071915,0.1385231,0.003314967,0.0002169335,0.0002685629,0.001150213,0.002166887,0.005121809],"genre_scores_gemma":[0.9553922,0.000086799,0.0429379,0.000337312,0.00002314478,0.00005067549,0.0004050235,0.0000142865,0.0007527048],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007523705,"threshold_uncertainty_score":0.01495987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008819933207628397,"score_gpt":0.3264828211819565,"score_spread":0.3176628879743281,"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."}}