{"id":"W3091644591","doi":"10.38126/jspg170119","title":"Artificial Intelligence Alongside Physicians in Canada: Reality and Risks","year":2020,"lang":"en","type":"article","venue":"Journal of Science Policy & Governance","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Interoperability; Accountability; Boosting (machine learning); Big data; Computer science; Data Protection Act 1998; Personally identifiable information; Data sharing; Information privacy; Internet privacy; Data science; Computer security; Business; Artificial intelligence; Political science; Data mining; Medicine; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.008851529,0.0004008474,0.0006104073,0.001825807,0.02741667,0.0116974,0.003084969,0.007909327,0.01050768],"category_scores_gemma":[0.03502331,0.0006521418,0.0006972392,0.004983873,0.01261658,0.0041474,0.006558882,0.01292879,0.0008968863],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.1642774,"about_ca_system_score_gemma":0.3869231,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.9877887,"about_ca_topic_score_gemma":0.9881661,"domain_scores_codex":[0.9766344,0.003364005,0.000700463,0.001578909,0.009737039,0.007985308],"domain_scores_gemma":[0.9467086,0.006067737,0.003945606,0.001275769,0.01477909,0.02722318],"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":[0.0004879796,0.0003936861,0.2086107,0.000451291,0.0001442762,0.00576486,0.02824113,0.001703907,0.0006820481,0.2350395,0.304599,0.2138816],"study_design_scores_gemma":[0.0002693154,0.0002485943,0.2059792,0.002891148,0.0001889409,0.003121214,0.08271559,0.005465924,0.0008351036,0.08691963,0.6107675,0.0005977592],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.05908357,0.00664759,0.0008545814,0.8875465,0.001060409,0.00007114463,0.0005004418,0.00005392397,0.04418171],"genre_scores_gemma":[0.8156409,0.01548027,0.001991828,0.1465748,0.0008816383,0.00005425681,0.000487883,0.0000767879,0.01881169],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.9877887,"threshold_uncertainty_score":0.9693198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2545298275549072,"score_gpt":0.4462575015330312,"score_spread":0.191727673978124,"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."}}