{"id":"W4413140769","doi":"10.1148/rg.240103","title":"Deep Learning Models Connecting Images and Text: A Primer for Radiologists","year":2025,"lang":"en","type":"article","venue":"Radiographics","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Centre Hospitalier de l’Université de Montréal; Western University","funders":"Canadian Institutes of Health Research; Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec; Radiological Society of North America","keywords":"Medicine; Primer (cosmetics); Deep learning; Artificial intelligence; Medical physics; Radiology; Natural language processing; Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003734544,0.00142286,0.0008202404,0.002092158,0.0003999677,0.003488563,0.003483277,0.003890302,0.008165705],"category_scores_gemma":[0.01113112,0.001953564,0.001196671,0.001605195,0.00186387,0.008031566,0.002326395,0.008345078,0.007124159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00137185,"about_ca_system_score_gemma":0.001198749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003304504,"about_ca_topic_score_gemma":0.00402269,"domain_scores_codex":[0.9992804,0.0002706287,0.00008070429,0.0001484327,0.0001805698,0.00003938453],"domain_scores_gemma":[0.9951757,0.002971687,0.0002280751,0.0004399803,0.0009077878,0.000276671],"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":[0.0001130354,0.000161478,0.001349657,0.0009720622,0.000146646,0.0002792779,0.0003133076,0.03100079,0.002896244,0.1125438,0.2042871,0.6459367],"study_design_scores_gemma":[0.00003068197,0.00009860063,0.0007306928,0.00104938,0.00006343536,0.0007635972,0.000134439,0.2205047,0.003742837,0.4215982,0.3511508,0.0001327439],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.0008059276,0.02775224,0.9350075,0.02718585,0.001110774,0.00007333856,0.0007844344,0.002848553,0.004431414],"genre_scores_gemma":[0.03828813,0.05996002,0.8650607,0.007401447,0.006187834,0.0008622976,0.002683242,0.001582393,0.01797397],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.008165705,"threshold_uncertainty_score":0.02731705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02581586918835225,"score_gpt":0.2688098754725671,"score_spread":0.2429940062842148,"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."}}