{"id":"W4407398500","doi":"10.3934/bioeng.2025004","title":"How artificial intelligence reduces human bias in diagnostics?","year":2025,"lang":"en","type":"article","venue":"AIMS bioengineering","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Lethbridge","funders":"","keywords":"Artificial intelligence; Computer science; Machine learning","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":[0.06386658,0.001650616,0.001764226,0.003735908,0.001232308,0.008293202,0.002798323,0.005087287,0.004359409],"category_scores_gemma":[0.1816079,0.0007814059,0.001268535,0.001684041,0.01476271,0.01044666,0.004753068,0.006253592,0.00148894],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003298672,"about_ca_system_score_gemma":0.003999119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002573333,"about_ca_topic_score_gemma":0.002167331,"domain_scores_codex":[0.9465899,0.03780638,0.002334043,0.004703303,0.007787289,0.0007791553],"domain_scores_gemma":[0.810425,0.1614636,0.006369511,0.01077702,0.009756506,0.001208354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004286548,0.0002027109,0.02151619,0.004924589,0.001507529,0.0004796509,0.002651714,0.02073934,0.001722541,0.3439724,0.02754808,0.5743067],"study_design_scores_gemma":[0.0001310676,0.0002314033,0.004118505,0.003378893,0.0003373072,0.0005800034,0.0006968501,0.02090722,0.002148674,0.8920968,0.07521497,0.0001582675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02154011,0.1076152,0.4881448,0.3263609,0.005072955,0.0004126606,0.0007104808,0.001345032,0.04879803],"genre_scores_gemma":[0.580871,0.06649648,0.2776743,0.05956832,0.007035004,0.0009982525,0.0006037445,0.0006354878,0.006117365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06386658,"threshold_uncertainty_score":0.3377628,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2135824500558615,"score_gpt":0.4180757778556072,"score_spread":0.2044933277997457,"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."}}