{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01355589,0.001210196,0.000937802,0.002787457,0.0006441772,0.004577354,0.002606337,0.002059303,0.004979344],"category_scores_gemma":[0.04421126,0.0008226807,0.002346248,0.001403844,0.001258974,0.005298657,0.0029302,0.003858672,0.002201793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002356959,"about_ca_system_score_gemma":0.003894223,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01010072,"about_ca_topic_score_gemma":0.01385783,"domain_scores_codex":[0.9959643,0.002395133,0.0003396017,0.0003843083,0.0007485541,0.0001681364],"domain_scores_gemma":[0.9729807,0.02049456,0.0007959634,0.001697673,0.003470196,0.0005610127],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002962971,0.0002351,0.0186011,0.006012226,0.0009347241,0.0006658038,0.001036883,0.1819322,0.004553711,0.1013265,0.1242952,0.5601103],"study_design_scores_gemma":[0.000114619,0.0003177902,0.004063149,0.003500648,0.0004464833,0.001003658,0.0008083666,0.4941873,0.005840613,0.1879715,0.3015231,0.0002227694],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01645084,0.0420716,0.8217245,0.08205172,0.002731742,0.0008122056,0.004953518,0.005550016,0.02365382],"genre_scores_gemma":[0.1785495,0.04463844,0.7435053,0.01493473,0.001872586,0.0009102797,0.009405878,0.0009191094,0.005264258],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01355589,"threshold_uncertainty_score":0.07169127,"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."}}