{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004694172,0.000128056,0.0001888716,0.0002954284,0.0002914922,0.0001531997,0.0003830054,0.00009150566,4.560411e-7],"category_scores_gemma":[0.0001635992,0.0001252836,0.00009184356,0.000478752,0.00006200532,0.000293135,0.0001507537,0.0002001988,2.562767e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001526595,"about_ca_system_score_gemma":0.00002672829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002761957,"about_ca_topic_score_gemma":0.000005868964,"domain_scores_codex":[0.9989423,0.00005960246,0.0001966516,0.0004309338,0.00009079059,0.0002797479],"domain_scores_gemma":[0.9990649,0.0004336716,0.00006795106,0.0003082319,0.00007301698,0.00005224883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001209982,0.00002845295,0.01142907,0.0001245041,0.0001212111,0.000006534406,0.001240748,0.04897669,0.0006558637,0.6190943,0.0001681089,0.3181424],"study_design_scores_gemma":[0.0003433859,0.00003395716,0.0005670931,0.00003149364,0.00001504934,0.00001332568,0.00005714959,0.9553519,0.000165494,0.04138958,0.001889488,0.0001420076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01635031,0.003959437,0.9775656,0.0006184498,0.0002053871,0.0002210377,5.692207e-7,0.0002303178,0.0008488575],"genre_scores_gemma":[0.7855062,0.0002645717,0.2138841,0.0001937033,0.00003808898,0.00002386144,0.000001328886,0.000007787881,0.00008032021],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9063753,"threshold_uncertainty_score":0.5108916,"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."}}