{"id":"W3159113244","doi":"10.20944/preprints202105.0056.v2","title":"Literature Analysis of Artificial Intelligence in Biomedicine","year":2021,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Standardization; Deep learning; Computer science; Big data; Biomedicine; Applications of artificial intelligence; Field (mathematics); Data science; Machine learning; Convolutional neural network; Data mining; Bioinformatics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001246521,0.0004524962,0.001797634,0.002670042,0.00002924711,0.0000285713,0.0005005695,0.0006779098,0.001860785],"category_scores_gemma":[0.00226759,0.0004536402,0.0007179979,0.004479907,0.0002216094,0.00006178278,0.001555793,0.001607752,0.00007486002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004182828,"about_ca_system_score_gemma":0.0006509434,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001011667,"about_ca_topic_score_gemma":0.0003181741,"domain_scores_codex":[0.9957498,0.0002134467,0.001424289,0.001478646,0.0007358081,0.0003979606],"domain_scores_gemma":[0.9959862,0.0003820744,0.0005092186,0.002323895,0.0005828007,0.0002158019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000299761,0.001836031,0.9280758,0.002386397,0.003308359,0.0008458524,0.009482542,0.007190144,0.03801129,0.0001818192,0.0000680977,0.008313933],"study_design_scores_gemma":[0.0002835401,0.00006070755,0.8927788,0.005962058,0.004245668,0.00001335337,0.0005299518,0.01022986,0.08361962,0.0005844682,0.001172435,0.0005195456],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9867284,0.001334157,0.0009903285,0.009274458,0.0005906494,0.0006720397,0.00005255223,0.00009658872,0.0002608146],"genre_scores_gemma":[0.9950464,0.0008648094,0.0008253406,0.002086908,0.0002235422,0.00009590956,0.0006585283,0.00004426968,0.0001542698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04560833,"threshold_uncertainty_score":0.9997916,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1495147062848282,"score_gpt":0.4174715833781985,"score_spread":0.2679568770933702,"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."}}