{"id":"W4229065849","doi":"10.1016/j.nbt.2022.05.002","title":"Explainability and causability for artificial intelligence-supported medical image analysis in the context of the European In Vitro Diagnostic Regulation","year":2022,"lang":"en","type":"article","venue":"New Biotechnology","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; Athabasca University","funders":"","keywords":"Context (archaeology); Relevance (law); Computer science; Usability; Artificial intelligence; Quality (philosophy); Data science; Domain (mathematical analysis); Artificial neural network; Machine learning; Human–computer interaction","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":[0.00243858,0.00007967478,0.000241554,0.0002414573,0.0001084,0.000005292008,0.0002162586,0.0001293541,0.0001194083],"category_scores_gemma":[0.005438335,0.00005504917,0.00007772796,0.001222449,0.0003845805,0.00002712044,0.00009675301,0.0004338598,0.000001081462],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001390123,"about_ca_system_score_gemma":0.0002260822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002916068,"about_ca_topic_score_gemma":0.004336794,"domain_scores_codex":[0.9982068,0.000492043,0.0006204475,0.0002727236,0.0002220576,0.0001859082],"domain_scores_gemma":[0.9983329,0.00100104,0.0001271714,0.0004408483,0.00005753687,0.00004047614],"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":[0.0003780371,0.0004986515,0.08916194,0.00006758711,0.00003333522,0.00001026758,0.006987548,0.00002759399,0.007907025,0.00269174,0.0001369206,0.8920994],"study_design_scores_gemma":[0.0002667148,0.0009592698,0.5189778,0.00005105089,0.0003836685,0.00007300307,0.06710593,0.01893663,0.3152598,0.07626294,0.00145362,0.0002695938],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9243641,0.00006844301,0.004214379,0.07029559,0.0001214318,0.0008786326,0.00001021531,0.00001630764,0.00003092911],"genre_scores_gemma":[0.9992101,0.00002027796,0.0001531193,0.0004428343,0.00004960782,0.00008235754,0.00002955699,0.000005615255,0.000006537285],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8918298,"threshold_uncertainty_score":0.6510587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07511568859992468,"score_gpt":0.3745188784383905,"score_spread":0.2994031898384659,"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."}}