{"id":"W3182648442","doi":"10.3389/fdata.2021.727856","title":"Editorial: ML and AI Safety, Effectiveness and Explainability in Healthcare","year":2021,"lang":"en","type":"editorial","venue":"Frontiers in Big Data","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Front (military); Health care; Public healthcare; Big data; Patient safety; Medicine; Political science; Engineering; Computer science; Data mining; Law","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":[0.008076163,0.003911933,0.003919791,0.004605922,0.003625794,0.008823602,0.003429806,0.01616843,0.03466478],"category_scores_gemma":[0.03530204,0.001149696,0.002777554,0.001628063,0.002472165,0.004442334,0.001656326,0.01680462,0.02087605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003180797,"about_ca_system_score_gemma":0.003490302,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001919901,"about_ca_topic_score_gemma":0.005514666,"domain_scores_codex":[0.9951676,0.0008668502,0.0006154473,0.0005665669,0.002472365,0.0003111624],"domain_scores_gemma":[0.9698379,0.013837,0.001434034,0.0006657386,0.01110193,0.003123356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002172216,0.000005582731,0.000009692524,0.00008508383,0.000009326865,0.000038023,0.000005774763,0.00001218702,0.00001437899,0.000206035,0.9973803,0.0022117],"study_design_scores_gemma":[0.0001184431,0.00002659667,0.0002249712,0.000699772,0.00006279071,0.0002185308,0.00004426073,0.0002309017,0.00009429932,0.002628632,0.9956273,0.0000235547],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"editorial","genre_scores_codex":[0.00002447553,0.003328975,0.0001438221,0.04844252,0.9457877,0.0000211487,0.0001240282,0.00007492134,0.002052454],"genre_scores_gemma":[0.0003998231,0.002591253,0.0001221453,0.01740162,0.9692891,0.00002808048,0.00006179682,0.00004980705,0.01005636],"genre_candidate":"editorial","genre_consensus":"editorial","teacher_disagreement_score":0.03466478,"threshold_uncertainty_score":0.1159652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09110361901950335,"score_gpt":0.4124540329371583,"score_spread":0.321350413917655,"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."}}