{"id":"W4385898316","doi":"10.1152/ajprenal.00177.2023","title":"“Hi, how can i help you?”: embracing artificial intelligence in kidney research","year":2023,"lang":"en","type":"review","venue":"American Journal of Physiology-Renal Physiology","topic":"Renal and Vascular Pathologies","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Kidney disease; Artificial intelligence; Machine learning; Nephrology; Kidney; Big data; Computer science; Acute kidney injury; Data science; Disease; Medicine; Intensive care medicine; Bioinformatics; Pathology; Data mining; Internal medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.009070771,0.000511073,0.0008012509,0.00180339,0.000938873,0.004503266,0.0008732628,0.003472305,0.003656006],"category_scores_gemma":[0.01323492,0.0002274776,0.0009466625,0.001620108,0.0038641,0.006849559,0.002330511,0.00603743,0.001786917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001739557,"about_ca_system_score_gemma":0.003662159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001523031,"about_ca_topic_score_gemma":0.003348353,"domain_scores_codex":[0.9960594,0.002433752,0.0002417823,0.0002771365,0.0008014992,0.0001864399],"domain_scores_gemma":[0.9909411,0.006711254,0.000337407,0.0001780974,0.001328662,0.0005034669],"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":[0.0001033295,0.00006856719,0.001656577,0.008731356,0.0001672901,0.0004199556,0.001836131,0.0003363776,0.0005566199,0.06248617,0.1765065,0.747131],"study_design_scores_gemma":[0.00002215564,0.00007961261,0.00160963,0.01278126,0.00008604244,0.001094268,0.001403756,0.0003112097,0.0002754627,0.08229243,0.8999835,0.00006064266],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000920194,0.7827165,0.005623143,0.1931744,0.00795524,0.00003315769,0.00006691762,0.00007526312,0.009435133],"genre_scores_gemma":[0.01707665,0.869419,0.01027785,0.08877685,0.01022102,0.00008621421,0.0000929931,0.00004669485,0.004002778],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009070771,"threshold_uncertainty_score":0.04797137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1595265594526886,"score_gpt":0.4253123794384398,"score_spread":0.2657858199857512,"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."}}