{"id":"W4280516831","doi":"10.2196/37365","title":"Combating COVID-19 Using Generative Adversarial Networks and Artificial Intelligence for Medical Images: Scoping Review","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":24,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Generative grammar; Adversarial system; Artificial intelligence; Computer science; Scarcity; 2019-20 coronavirus outbreak; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Computed tomography; Machine learning; Radiology; Medicine; Pathology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003750815,0.00026776,0.0007639874,0.000159497,0.0006977226,0.00006003014,0.0003130494,0.000217447,0.001581466],"category_scores_gemma":[0.01109248,0.0002463944,0.000154965,0.0005094264,0.0004048415,0.000189544,0.0006045472,0.0009735133,0.000004312626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004856801,"about_ca_system_score_gemma":0.003442683,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004038012,"about_ca_topic_score_gemma":0.00001643859,"domain_scores_codex":[0.9954544,0.0002103414,0.001665236,0.0002646644,0.00188892,0.0005164364],"domain_scores_gemma":[0.995432,0.002446426,0.0004620997,0.0003231115,0.0001471976,0.001189136],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0018198,0.002548325,0.0009346085,0.164324,0.0009135517,0.00103174,0.03490088,0.02738605,0.00006954031,0.01237498,0.2635922,0.4901044],"study_design_scores_gemma":[0.001268333,0.0004654564,0.00000361275,0.02009496,0.0002209948,0.00042131,0.002190277,0.9631371,0.00005490857,0.0003438669,0.01142655,0.0003726128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"commentary","genre_scores_codex":[0.004560188,0.00581554,0.8946947,0.0875437,0.001186913,0.005787014,0.00005009963,0.0002654853,0.000096364],"genre_scores_gemma":[0.03381684,0.007301149,0.04890343,0.9044318,0.002841231,0.002027253,0.0005268137,0.0001245526,0.00002698566],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9357511,"threshold_uncertainty_score":0.9999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09526057330090464,"score_gpt":0.4365261673392923,"score_spread":0.3412655940383877,"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."}}