{"id":"W4416588311","doi":"10.2196/66931","title":"Impact of AI on Breast Cancer Detection Rates in Mammography by Radiologists of Varying Experience Levels in Singapore: Preliminary Comparative Study","year":2025,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Generalizability theory; Mammography; Breast cancer; Breast cancer screening; Mammography screening; Screening mammography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002349173,0.0003550586,0.000304796,0.001279986,0.0003185023,0.0007762049,0.0003855167,0.0004395508,0.001462744],"category_scores_gemma":[0.008992301,0.0002646269,0.000613322,0.001153791,0.0004580664,0.0007491587,0.0009060964,0.0002665625,0.0004131684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007360129,"about_ca_system_score_gemma":0.0004370875,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005557591,"about_ca_topic_score_gemma":0.006472179,"domain_scores_codex":[0.9978901,0.0008627946,0.0003524054,0.0002993811,0.0003688615,0.0002265868],"domain_scores_gemma":[0.990027,0.003187501,0.003448789,0.0005440035,0.001731184,0.001061539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002407436,0.00006851514,0.9964372,0.00002108746,0.00004969427,0.0001400814,0.0006101548,0.00003833796,0.0001707553,0.000005623915,0.00003843659,0.002179248],"study_design_scores_gemma":[0.000008190944,0.0005811923,0.9980375,0.000004522576,0.00003070595,0.0002354786,0.0007557115,0.0001458051,0.0000990382,0.000004665057,0.00009221573,0.00000499967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996686,0.00004921965,0.00002037744,0.00001100261,0.000001260945,0.000005057825,0.00005147319,0.000001783923,0.0001911507],"genre_scores_gemma":[0.9997513,0.00003528986,0.00004931567,0.00000740206,0.000002734605,0.000006280502,0.00008436108,9.399327e-7,0.0000623336],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005557591,"threshold_uncertainty_score":0.01242375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07915726736223347,"score_gpt":0.4736729420918332,"score_spread":0.3945156747295998,"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."}}