{"id":"W2985205772","doi":"10.1042/etls20190106","title":"Artificial Intelligence and global health: opportunities and challenges","year":2019,"lang":"en","type":"review","venue":"Emerging Topics in Life Sciences","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Transformative learning; Humanity; Sustainable development; Engineering ethics; Global health; Global challenges; Political science; Psychology; Knowledge management; Computer science; Health care; Engineering; Developmental psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.001238331,0.0002127097,0.0008525933,0.0002352631,0.0001834454,0.00004511295,0.0001499963,0.0001668224,0.00002465846],"category_scores_gemma":[0.0003934432,0.0001738702,0.00006042866,0.0003458661,0.0004545656,0.0001171365,0.00008804078,0.00025548,0.00001138024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001192002,"about_ca_system_score_gemma":0.001720505,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004402658,"about_ca_topic_score_gemma":0.0002347557,"domain_scores_codex":[0.9979635,0.0001329197,0.0007842907,0.0005005618,0.000265323,0.000353405],"domain_scores_gemma":[0.9991104,0.0002122478,0.0002253608,0.0001926232,0.00004455879,0.0002147992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000001597501,0.00001971225,0.0001136667,0.007835737,0.000006752723,0.000002576209,0.0007989805,8.426502e-7,1.75534e-9,0.02982694,0.00006233218,0.9613309],"study_design_scores_gemma":[0.000004767609,0.0002824336,0.00005836415,0.0082247,0.00005081198,0.00005393157,0.006867363,0.0001506185,3.597814e-7,0.009387059,0.9747072,0.0002123516],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000139957,0.9708965,0.00002813143,0.02623845,0.001237719,0.0004637229,0.000005115478,0.00002280247,0.0009675392],"genre_scores_gemma":[0.0002118199,0.9981237,0.0004693331,0.0004289977,0.0006340413,0.00002263253,0.000006053316,0.000008006565,0.00009540969],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9746449,"threshold_uncertainty_score":0.7090221,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7336957573661195,"score_gpt":0.5454399562865961,"score_spread":0.1882558010795233,"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."}}