{"id":"W4411975894","doi":"10.3390/diagnostics15131698","title":"Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection","year":2025,"lang":"en","type":"article","venue":"Diagnostics","topic":"Poxvirus research and outbreaks","field":"Immunology and Microbiology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Toronto","funders":"National Research Foundation of Korea; Kumoh National Institute of Technology; National Research Foundation","keywords":"Interpretability; Computer science; Artificial intelligence; Machine learning; Convolutional neural network; Transfer of learning; Deep learning; Pattern recognition (psychology); Data mining","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":[0.002035693,0.001370784,0.0006054977,0.001135428,0.0003535388,0.001908361,0.001653384,0.001846547,0.003113204],"category_scores_gemma":[0.009672809,0.0004172897,0.0009768839,0.0003485646,0.0008485359,0.00186309,0.001506466,0.001654976,0.001636163],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001067738,"about_ca_system_score_gemma":0.001159352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003285571,"about_ca_topic_score_gemma":0.003258616,"domain_scores_codex":[0.99925,0.0001535167,0.00002984829,0.000238394,0.0002245552,0.000103652],"domain_scores_gemma":[0.9980118,0.0009341377,0.0002311343,0.0002312646,0.0004700287,0.0001215664],"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":[0.000792375,0.0003493541,0.02794171,0.000696927,0.0002547597,0.0006137626,0.0002528772,0.09754192,0.07469937,0.003503786,0.0126305,0.7807227],"study_design_scores_gemma":[0.00003971657,0.0007194784,0.007930837,0.0001283001,0.0001589878,0.00161975,0.0001396865,0.9045009,0.06866942,0.007911301,0.008125375,0.00005626916],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3217048,0.005507272,0.6446974,0.003736292,0.0007822192,0.0004861068,0.0006582048,0.01323027,0.0091975],"genre_scores_gemma":[0.897054,0.0009228522,0.09681354,0.0009249465,0.0001536952,0.00008100265,0.0006054769,0.0002958089,0.003148815],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003285571,"threshold_uncertainty_score":0.01076591,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006945192370781756,"score_gpt":0.2679890247542259,"score_spread":0.2610438323834441,"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."}}