{"id":"W3170544780","doi":"10.21428/594757db.62860442","title":"A User-Centered Design of Explainable AI for Clinical Decision Support","year":2021,"lang":"en","type":"article","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Interpretability; Computer science; Construct (python library); Context (archaeology); Process (computing); Artificial intelligence; Decision support system; Machine learning; Human–computer interaction; Data science","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":[0.001565796,0.0001465789,0.0003463658,0.00009189568,0.0001148767,0.0001464875,0.0009474867,0.0001165719,0.0002439185],"category_scores_gemma":[0.001141714,0.0001355977,0.0002064949,0.0005000466,0.0000608736,0.0007401905,0.0003944826,0.0001134142,0.0001314732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003763942,"about_ca_system_score_gemma":0.0004501437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002196389,"about_ca_topic_score_gemma":0.00002592068,"domain_scores_codex":[0.9976018,0.0001639283,0.0008604723,0.0006099154,0.0003207999,0.000443101],"domain_scores_gemma":[0.9965639,0.001436515,0.0001446913,0.0009628647,0.0007346333,0.000157459],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004131509,0.001986818,0.003466435,0.0001006502,0.0001129486,0.0003254316,0.001012326,0.002316565,0.007943648,0.4643831,0.2004962,0.3174427],"study_design_scores_gemma":[0.001176554,0.001473708,0.0003871383,0.00009053208,0.00002535629,0.000058689,0.00069002,0.2759005,0.5375348,0.07984301,0.1022775,0.0005421552],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003425942,0.00006069133,0.9935097,0.000952595,0.0006184611,0.0003837375,0.000002703426,0.0001012993,0.0009449273],"genre_scores_gemma":[0.1769966,0.0001156637,0.8169157,0.00173168,0.00008110045,0.00005776132,0.000005244144,0.00001932256,0.004076921],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5295911,"threshold_uncertainty_score":0.5529514,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1535379592912031,"score_gpt":0.4066577552445856,"score_spread":0.2531197959533825,"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."}}