{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02442769,0.0005468571,0.0005396537,0.003332248,0.0006168836,0.002264051,0.0007761395,0.001058249,0.001585245],"category_scores_gemma":[0.1042138,0.0004703404,0.0006597099,0.001240516,0.001122316,0.001746676,0.002551584,0.0007445883,0.0005973177],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001285918,"about_ca_system_score_gemma":0.0007736528,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001854695,"about_ca_topic_score_gemma":0.00232304,"domain_scores_codex":[0.9761901,0.0114752,0.002552387,0.004547548,0.004411331,0.0008233034],"domain_scores_gemma":[0.837088,0.1148597,0.02196418,0.005639972,0.01780871,0.002639474],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007399367,0.0001667017,0.870313,0.0003548952,0.0003630435,0.0003908097,0.003371684,0.002287521,0.006489649,0.0001270648,0.001124887,0.1142709],"study_design_scores_gemma":[0.00008437842,0.001308171,0.9636376,0.0002018204,0.0002916954,0.003496093,0.002075937,0.01783544,0.007071542,0.00102885,0.002861605,0.0001068521],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9796497,0.001478256,0.01142951,0.0007116571,0.00005831812,0.000140206,0.0001431392,0.0003837074,0.006005597],"genre_scores_gemma":[0.9922769,0.0001275547,0.007191502,0.000121165,0.00005147501,0.00002457252,0.00006250594,0.00002345115,0.000120719],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02442769,"threshold_uncertainty_score":0.1291875,"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."}}