{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005267335,0.0002753285,0.0002754662,0.00008624035,0.00009409287,0.00007074662,0.0003813792,0.0004419999,0.000100412],"category_scores_gemma":[0.0003329506,0.0002271657,0.0001799856,0.0001505409,0.0003063928,0.000006100234,0.00008210374,0.0003151194,0.0001207354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003098058,"about_ca_system_score_gemma":0.0007447553,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000440028,"about_ca_topic_score_gemma":0.0002678094,"domain_scores_codex":[0.9979951,0.00005087324,0.0003404595,0.0006306274,0.0003340846,0.0006488604],"domain_scores_gemma":[0.9990941,0.0001437289,0.00005055874,0.0003903201,0.00009455659,0.0002267352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002692885,0.0001871296,0.000163364,0.0003838739,0.0007068685,0.00007218377,0.0002143416,0.00005914815,0.3175902,0.00126663,0.2890395,0.3900474],"study_design_scores_gemma":[0.0009549545,0.0004436721,0.00003212609,0.0001098965,0.0001423585,0.0001183417,0.00002476837,0.1118105,0.02787712,0.000525967,0.8574702,0.0004900864],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4090039,0.3509278,0.22747,0.006687009,0.001933425,0.0009281051,0.0002933372,0.000565362,0.002191019],"genre_scores_gemma":[0.98299,0.003536642,0.003889614,0.001479497,0.0003814637,0.0001381808,0.0002753763,0.00007989031,0.007229372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.573986,"threshold_uncertainty_score":0.9263548,"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."}}