{"id":"W4413812319","doi":"10.2196/75015","title":"COVID-19 Pneumonia Diagnosis Using Medical Images: Deep Learning–Based Transfer Learning Approach","year":2025,"lang":"en","type":"article","venue":"JMIRx Med","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Coronavirus disease 2019 (COVID-19); Transfer of learning; Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Pneumonia; 2019-20 coronavirus outbreak; Deep learning; Artificial intelligence; Computer science; Virology; Medicine; Infectious disease (medical specialty); Pathology; World Wide Web; Internal medicine","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":[0.00158229,0.0004976445,0.0009295485,0.0007411031,0.0005449389,0.0001001585,0.0003948587,0.0005417858,0.001375493],"category_scores_gemma":[0.01468897,0.0004780169,0.0004001377,0.001371842,0.000369016,0.000125804,0.0001190172,0.001732671,0.00005374997],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001080803,"about_ca_system_score_gemma":0.002872106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006165503,"about_ca_topic_score_gemma":0.00004600077,"domain_scores_codex":[0.9954053,0.0006625383,0.0007442329,0.001030494,0.001323996,0.0008334178],"domain_scores_gemma":[0.9955137,0.002350135,0.0001071026,0.0005914596,0.0002198397,0.001217773],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002351486,0.008133199,0.5997766,0.01263165,0.0017278,0.003071887,0.008095392,0.1787131,0.02681109,0.0005989321,0.09013189,0.06795698],"study_design_scores_gemma":[0.01032141,0.0004664786,0.01130086,0.00129946,0.001067675,0.0000869393,0.0007447347,0.2309221,0.005552482,0.0000540951,0.7372583,0.0009254948],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3801622,0.002270891,0.2402468,0.3676159,0.0009284749,0.003414053,0.00001444493,0.002531283,0.002815871],"genre_scores_gemma":[0.9231963,0.0002258318,0.002417423,0.07023861,0.0002914545,0.0006529539,0.0001235453,0.0001323644,0.002721584],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6471264,"threshold_uncertainty_score":0.9997671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03587313905863811,"score_gpt":0.3548877279588579,"score_spread":0.3190145889002198,"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."}}