{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.005042114,0.00096653,0.002218597,0.0524416,0.001205476,0.004667426,0.001675517,0.002053427,0.01951917],"category_scores_gemma":[0.03679178,0.0004874223,0.002127082,0.07003744,0.001654895,0.004038726,0.002022943,0.001982649,0.004188258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004124823,"about_ca_system_score_gemma":0.01127366,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004142988,"about_ca_topic_score_gemma":0.006290525,"domain_scores_codex":[0.9932618,0.001472257,0.001931136,0.0006314323,0.002467229,0.0002362199],"domain_scores_gemma":[0.9395102,0.04478469,0.004912183,0.0009586182,0.009084702,0.0007495762],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001431952,0.00005490969,0.003224553,0.204757,0.0007500317,0.0008464155,0.001802974,0.0005695632,0.0006351449,0.02210263,0.1636682,0.6014454],"study_design_scores_gemma":[0.00002031126,0.00004939627,0.00706085,0.2062289,0.001045702,0.001267429,0.001149319,0.0002086333,0.0003486894,0.008471682,0.7740984,0.00005062707],"study_design_candidate":"systematic_review","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008651849,0.9806257,0.0007098007,0.00522236,0.001476924,0.00006559498,0.001471547,0.00003563951,0.009527169],"genre_scores_gemma":[0.005264885,0.9875624,0.001300232,0.002272412,0.001214691,0.00009204829,0.001246537,0.00002360967,0.001023158],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9475584,"threshold_uncertainty_score":0.06529814,"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."}}