{"id":"W3013443134","doi":"10.1038/s41746-020-0254-2","title":"Machine intelligence in healthcare—perspectives on trustworthiness, explainability, usability, and transparency","year":2020,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":325,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Center for Advancing Translational Sciences; Agency for Healthcare Research and Quality; National Institutes of Health; University of Toronto; National Institute of Biomedical Imaging and Bioengineering; Vanderbilt University; York University; Vanderbilt University Medical Center; Johns Hopkins University; Massachusetts Institute of Technology","keywords":"Transparency (behavior); Workflow; Health care; Usability; Context (archaeology); Quality (philosophy); Data science; Knowledge management; Medicine; Computer science; Political science; Human–computer interaction; Computer security","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003231165,0.0002513314,0.0005392819,0.0001520695,0.00007492411,0.00002466472,0.0001222446,0.0001271034,0.0001634154],"category_scores_gemma":[0.002428774,0.0001974224,0.00005247921,0.0006633122,0.0004653587,0.0002535465,0.00003284011,0.0005221777,0.00003242173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001714149,"about_ca_system_score_gemma":0.0001660311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001236647,"about_ca_topic_score_gemma":0.0001616351,"domain_scores_codex":[0.9977157,0.00005820108,0.0007956874,0.0006647903,0.0004028027,0.0003628114],"domain_scores_gemma":[0.9984939,0.0003897922,0.00008658737,0.0003080527,0.0001845483,0.0005371567],"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":[0.001094739,0.0005737952,0.4222727,0.000872123,0.00001809997,0.00006776685,0.03548801,0.0000204078,0.00005784551,0.01126463,0.0002221633,0.5280477],"study_design_scores_gemma":[0.002455545,0.04047217,0.5657995,0.00531546,0.0002413138,0.0004239176,0.1944052,0.01437344,0.004104753,0.153798,0.01613562,0.002475136],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7244193,0.002872659,0.0009897845,0.2656887,0.0002634516,0.001026681,0.0000177592,0.0001177459,0.004603909],"genre_scores_gemma":[0.9947339,0.0008276844,0.0000843001,0.003736783,0.0004723059,0.00003990497,0.00004094817,0.00002326375,0.00004092433],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5255726,"threshold_uncertainty_score":0.8050652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.122824430273791,"score_gpt":0.403696107216324,"score_spread":0.2808716769425331,"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."}}