{"id":"W4400976648","doi":"10.1148/rg.240167","title":"Overcoming “Fear of AI” Bias: Insights from the Technology Acceptance Model","year":2024,"lang":"en","type":"letter","venue":"Radiographics","topic":"Digital Transformation in Law","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal","funders":"Fonds de Recherche du Québec - Santé; Fondation de l'Association des radiologistes du Québec","keywords":"Medicine","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":[0.01287562,0.0004431055,0.0009478267,0.001315291,0.00246863,0.006681749,0.002417314,0.009704146,0.01353636],"category_scores_gemma":[0.07133713,0.0002922066,0.0009450075,0.001241587,0.01044538,0.008009118,0.002358717,0.007669362,0.001109037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003565379,"about_ca_system_score_gemma":0.002738235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005484307,"about_ca_topic_score_gemma":0.004162414,"domain_scores_codex":[0.9908833,0.005812156,0.0002020158,0.000682009,0.00162052,0.0007999648],"domain_scores_gemma":[0.9327334,0.05526239,0.003740893,0.003369838,0.003690951,0.001202436],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003635194,0.000047166,0.001455559,0.00002452018,0.00001742468,0.0001237077,0.0009441948,0.001867682,0.00008439821,0.9835097,0.004632219,0.007257096],"study_design_scores_gemma":[0.00003751026,0.00002210298,0.00101413,0.00002707887,0.00001427221,0.0001051482,0.000676016,0.01806136,0.0001619716,0.9726864,0.007171954,0.00002197808],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"commentary","genre_scores_codex":[0.1540472,0.001196794,0.1537661,0.2772988,0.000494707,0.0001453315,0.0001943898,0.0002008625,0.4126557],"genre_scores_gemma":[0.9790814,0.0002977446,0.004559384,0.007240525,0.0004103408,0.00005677035,0.00001782706,0.00004767708,0.008288207],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.01353636,"threshold_uncertainty_score":0.0680936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03968919854276514,"score_gpt":0.2262174986127319,"score_spread":0.1865283000699668,"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."}}