{"id":"W3131158873","doi":"10.21203/rs.3.rs-151985/v1","title":"Medical imaging algorithms exacerbate biases in underdiagnosis","year":2021,"lang":"en","type":"preprint","venue":"Research Square","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Vector Institute; University of Toronto","funders":"","keywords":"Algorithm; Computer science; Medical imaging; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003626858,0.0002116063,0.0004874734,0.0008004073,0.0001567429,0.0001521119,0.0002792286,0.0004890964,0.002414332],"category_scores_gemma":[0.01060405,0.0002046768,0.0001652088,0.0008726539,0.000247004,0.00008374062,0.0007153899,0.003268539,0.0001489674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009909238,"about_ca_system_score_gemma":0.00553004,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02318609,"about_ca_topic_score_gemma":0.003479452,"domain_scores_codex":[0.9947948,0.00071485,0.0006664601,0.0007795149,0.002104653,0.0009396576],"domain_scores_gemma":[0.9952702,0.002139073,0.00006796556,0.0007525415,0.001102047,0.0006681512],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0000744323,0.0009199091,0.2978165,0.002804189,0.00004275898,0.002358442,0.0044283,0.0001513278,0.00003991886,0.00004823273,0.008732675,0.6825833],"study_design_scores_gemma":[0.0008462359,0.0009122264,0.3676951,0.119019,0.0001608599,0.0005661499,0.2533875,0.1949802,0.02874471,0.01290106,0.01858091,0.002206124],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9316599,0.01168106,0.0002544802,0.0520986,0.001150576,0.001342321,0.00001595923,0.00009144074,0.00170561],"genre_scores_gemma":[0.9860418,0.01070171,0.0003220913,0.0004866959,0.001283845,0.0004768177,0.0003921138,0.00004888585,0.0002460661],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6803771,"threshold_uncertainty_score":0.9990309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4034576381905601,"score_gpt":0.5734717171442163,"score_spread":0.1700140789536562,"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."}}