{"id":"W3182457246","doi":"10.3389/fhumd.2021.673104","title":"On Assessing Trustworthy AI in Healthcare. Machine Learning as a Supportive Tool to Recognize Cardiac Arrest in Emergency Calls","year":2021,"lang":"en","type":"article","venue":"Frontiers in Human Dynamics","topic":"Cardiac Arrest and Resuscitation","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cégep André Laurendeau; Western University; Université du Québec à Montréal","funders":"","keywords":"Health care; Context (archaeology); Harm; Variety (cybernetics); Artificial intelligence; Knowledge management; Computer science; Engineering ethics; Medicine; Engineering; Political science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006610499,0.0002400947,0.0006782448,0.0006139681,0.00009548935,0.00003799791,0.00007895091,0.0002046983,0.00003549134],"category_scores_gemma":[0.000640375,0.0002646659,0.0001712899,0.0009627415,0.00003476326,0.0001428008,0.00006255387,0.001057319,0.000009646701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098921,"about_ca_system_score_gemma":0.0002946769,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001252402,"about_ca_topic_score_gemma":0.006398295,"domain_scores_codex":[0.9975552,0.000336602,0.0007006382,0.0005488733,0.0003603927,0.0004982775],"domain_scores_gemma":[0.9992619,0.00005641531,0.0001234325,0.000277156,0.000120833,0.0001602899],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002146128,0.0001645724,0.9739754,0.0001461554,0.00004477759,0.0006140325,0.001441048,0.002248847,0.0001371342,0.0004420655,0.001304933,0.0192664],"study_design_scores_gemma":[0.001941161,0.0002134423,0.9632737,0.0008213383,0.00005029,0.000002723359,0.002391816,0.02444725,0.00003829664,0.005651385,0.000690907,0.0004776626],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9878989,0.0004680328,0.001178513,0.002810834,0.005508161,0.0006189302,0.00002360506,0.0000386522,0.001454353],"genre_scores_gemma":[0.9956367,0.0002861968,0.001800612,0.0005650586,0.0001959594,0.00006364106,0.0007249328,0.00005487441,0.0006720227],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02219841,"threshold_uncertainty_score":0.9999806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009717560270967505,"score_gpt":0.3081653970289798,"score_spread":0.2984478367580123,"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."}}