{"id":"W4413722016","doi":"10.3390/diagnostics15172145","title":"AI Enhances Lung Ultrasound Interpretation Across Clinicians with Varying Expertise Levels","year":2025,"lang":"en","type":"article","venue":"Diagnostics","topic":"Ultrasound in Clinical Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Lung ultrasound; Interpretation (philosophy); Ultrasound; Lung; Medicine; Medical physics; Radiology; Computer science; Psychology; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002161404,0.0001765835,0.0003234762,0.00005254416,0.0001764933,0.00007001304,0.000159817,0.0001448026,0.0001075911],"category_scores_gemma":[0.005069936,0.0001462904,0.0000851539,0.0003886337,0.0002344815,0.0001207273,0.00003844042,0.0003486789,0.00006732281],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001004798,"about_ca_system_score_gemma":0.0001570058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002922057,"about_ca_topic_score_gemma":0.00004641704,"domain_scores_codex":[0.9985421,0.00003645998,0.0005240559,0.0003783093,0.0002170609,0.0003019911],"domain_scores_gemma":[0.9922953,0.006764856,0.0001184635,0.0004520318,0.0002586617,0.0001106439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0008706256,0.002622129,0.8697377,0.0006261175,0.0008989473,0.00006536979,0.01097743,0.0003727435,0.01973315,0.005662175,0.03512856,0.05330504],"study_design_scores_gemma":[0.007941927,0.001513482,0.826816,0.005289315,0.001678684,0.0001074316,0.002918069,0.007341291,0.09686562,0.008138035,0.03999552,0.001394578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7352839,0.0008561619,0.2500836,0.006387516,0.0005016361,0.0009366501,0.0001548911,0.0001997525,0.005595987],"genre_scores_gemma":[0.9831566,0.0006857508,0.005049693,0.009987325,0.0001589728,0.0002017511,0.0001053844,0.00002489094,0.0006296614],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2478727,"threshold_uncertainty_score":0.6069553,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02558357167003933,"score_gpt":0.4026126234533113,"score_spread":0.377029051783272,"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."}}