{"id":"W4386369761","doi":"10.3390/biomedinformatics3030045","title":"Deep Learning and Federated Learning for Screening COVID-19: A Review","year":2023,"lang":"en","type":"review","venue":"BioMedInformatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Deep learning; Artificial intelligence; Computer science; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Machine learning; Medical physics; Data science; Computed tomography; Medicine; Radiology; Disease; Infectious disease (medical specialty); Pathology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003658543,0.001075171,0.001893114,0.002997871,0.0002596779,0.00126274,0.00123349,0.001206758,0.002800735],"category_scores_gemma":[0.0117199,0.0003952708,0.002840248,0.002852249,0.0003833433,0.001274405,0.0008099735,0.001225587,0.0006225265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001046426,"about_ca_system_score_gemma":0.003060187,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003890215,"about_ca_topic_score_gemma":0.005120587,"domain_scores_codex":[0.998861,0.0004558463,0.000217742,0.0001783364,0.0002359189,0.00005122176],"domain_scores_gemma":[0.9942679,0.004835871,0.0003029219,0.00009036453,0.0004378289,0.00006497314],"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":[0.00017416,0.00007768629,0.001073947,0.0729655,0.001923187,0.00005685678,0.00005832234,0.002133552,0.000209634,0.002888336,0.008812221,0.9096267],"study_design_scores_gemma":[0.0003531152,0.001870584,0.01021454,0.233979,0.02285028,0.001801852,0.0003716988,0.01060982,0.002719748,0.02153073,0.6934472,0.0002515635],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002496373,0.9979053,0.0008378365,0.000448733,0.00007243129,0.00001706049,0.00006994433,0.00001151209,0.0003876134],"genre_scores_gemma":[0.004549086,0.9926293,0.001907232,0.0004554673,0.0001243697,0.00004320582,0.000132029,0.000005038722,0.0001542978],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003890215,"threshold_uncertainty_score":0.0193485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1447354331048505,"score_gpt":0.4384138844730965,"score_spread":0.293678451368246,"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."}}