{"id":"W4405778859","doi":"10.1109/mce.2024.3522521","title":"MedVLM: Medical Vision–Language Model for Consumer Devices","year":2024,"lang":"en","type":"article","venue":"IEEE Consumer Electronics Magazine","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer 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":[0.001036681,0.0007777321,0.0005371225,0.0008743448,0.0002727853,0.001177514,0.001787381,0.001292719,0.005031273],"category_scores_gemma":[0.003825232,0.0004220982,0.001655496,0.0004854998,0.0003627578,0.001124437,0.001297136,0.001429369,0.00215944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156089,"about_ca_system_score_gemma":0.001291838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01174628,"about_ca_topic_score_gemma":0.01571613,"domain_scores_codex":[0.9995182,0.0001440772,0.00003458827,0.0001391955,0.0001219469,0.00004212249],"domain_scores_gemma":[0.999463,0.0003263156,0.00003657639,0.00006543595,0.00008027609,0.00002841333],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004175868,0.0002354494,0.002972039,0.0004372778,0.0002145859,0.0004137687,0.0002624312,0.4661816,0.008294191,0.02540401,0.06511719,0.4300499],"study_design_scores_gemma":[0.00003254172,0.00004037654,0.0002137431,0.00002170405,0.00001294475,0.00008714109,0.0000168534,0.9776118,0.001359635,0.0115507,0.009036947,0.00001566731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01327448,0.001215473,0.9587234,0.001222471,0.0002347925,0.0002315284,0.00373111,0.01793858,0.003428257],"genre_scores_gemma":[0.3463407,0.0009953859,0.6244398,0.002000345,0.0001802109,0.0009994213,0.01302674,0.001455082,0.01056234],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01174628,"threshold_uncertainty_score":0.02335584,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01401059084113875,"score_gpt":0.3208812965795662,"score_spread":0.3068707057384275,"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."}}