{"id":"W2981947730","doi":"10.1016/b978-0-12-816176-0.00038-7","title":"Human–machine interfaces for medical imaging and clinical interventions","year":2019,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Controllability; Interface (matter); Human–computer interaction; Observability; Context (archaeology); Task (project management); Domain (mathematical analysis); Human–machine system; User interface; Systems engineering; Engineering; Programming language","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":[0.000479501,0.001197038,0.0005903513,0.00133885,0.0003209209,0.002584202,0.0009771149,0.001799081,0.08568882],"category_scores_gemma":[0.001110484,0.0004049043,0.0003606614,0.001299475,0.0006803391,0.002702993,0.001306754,0.001506055,0.04220866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004390177,"about_ca_system_score_gemma":0.0004967313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009638903,"about_ca_topic_score_gemma":0.001752585,"domain_scores_codex":[0.9996743,0.00005208223,0.00001853809,0.00005188917,0.0001854652,0.00001768224],"domain_scores_gemma":[0.9996159,0.0002212425,0.00001490512,0.00004153965,0.00008555606,0.00002083524],"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":[0.00001805355,0.00002696762,0.0000531931,0.0005124879,0.00001537677,0.00007631224,0.0001210012,0.00109345,0.003405376,0.03941606,0.2007989,0.7544628],"study_design_scores_gemma":[0.000005343402,0.00002520262,0.0003267362,0.0003636439,0.00001214562,0.0004945677,0.00006022827,0.003984173,0.001608344,0.03770591,0.9553963,0.00001736408],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.001697289,0.1036713,0.2745988,0.004097194,0.006616772,0.0001279202,0.0005942943,0.003138968,0.6054574],"genre_scores_gemma":[0.0105657,0.03987718,0.06367317,0.001440751,0.001710003,0.000114458,0.0005865821,0.0005398085,0.8814923],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.08568882,"threshold_uncertainty_score":0.2866577,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1676193698020237,"score_gpt":0.4856402713446811,"score_spread":0.3180209015426575,"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."}}