{"id":"W4309475162","doi":"10.1038/s41467-022-34234-4","title":"Developing medical imaging AI for emerging infectious diseases","year":2022,"lang":"en","type":"article","venue":"Nature Communications","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"U.S. National Library of Medicine; National Heart, Lung, and Blood Institute; Stanford Bio-X; School of Medicine, Stanford University; U.S. Department of Health and Human Services","keywords":"Software deployment; Workflow; Coronavirus disease 2019 (COVID-19); Context (archaeology); Data science; Computer science; Pandemic; Health care; Strengths and weaknesses; Applications of artificial intelligence; Infectious disease (medical specialty); Artificial intelligence; Medicine; Disease; Pathology; Psychology; Software engineering; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003620569,0.0001126968,0.000189683,0.0002205181,0.001144947,0.00002727712,0.0006650949,0.00007695724,0.0001488922],"category_scores_gemma":[0.00240309,0.0001217818,0.0001207705,0.0005318877,0.00009382205,0.00007229317,0.0008885103,0.001250476,0.000006051011],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005221816,"about_ca_system_score_gemma":0.0008749022,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004316371,"about_ca_topic_score_gemma":0.0001072455,"domain_scores_codex":[0.9987932,0.0001261735,0.0002370131,0.0002148415,0.0004155244,0.0002132523],"domain_scores_gemma":[0.9971461,0.001214903,0.00008040827,0.001218368,0.0002082334,0.0001319408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008381093,0.001384671,0.2297664,0.0002824007,0.0002940822,0.00003793232,0.001283421,0.0001901252,0.0002745433,0.08273157,0.6029467,0.08072437],"study_design_scores_gemma":[0.0009044881,0.00002310024,0.01075781,0.00009113948,0.0001253269,0.00006719943,0.0001715738,0.009016316,0.00002280633,0.0008954165,0.9777763,0.0001485356],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.003913852,0.01661951,0.005826937,0.9711862,0.0006473317,0.0008018998,0.00009023179,0.0005058256,0.0004081991],"genre_scores_gemma":[0.8720078,0.0003107384,0.003302396,0.123104,0.0001235405,0.0006504247,0.0004128797,0.00003495001,0.00005332435],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.8680939,"threshold_uncertainty_score":0.8806123,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667720372612997,"score_gpt":0.4002482977277559,"score_spread":0.373571094001626,"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."}}