{"id":"W4400110890","doi":"10.2196/52639","title":"Artificial Intelligence for Optimizing Cancer Imaging: User Experience Study","year":2024,"lang":"en","type":"article","venue":"JMIR Cancer","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Preprint; Artificial intelligence; Computer science; Machine learning; User experience design; Human–computer interaction; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0002154955,0.0001759464,0.0002232932,0.0001482698,0.0001704308,0.0001214037,0.0001202877,0.00006295006,0.0009237963],"category_scores_gemma":[0.00006540072,0.0001534065,0.0001022537,0.0004271311,0.00008341775,0.0002304112,0.00002804785,0.0002195933,0.0000606978],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003622684,"about_ca_system_score_gemma":0.0005704201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00222581,"about_ca_topic_score_gemma":0.0005460485,"domain_scores_codex":[0.9983304,0.00002239225,0.0004838662,0.0005189475,0.0002488701,0.000395542],"domain_scores_gemma":[0.999148,0.0001399851,0.00005167939,0.0002626563,0.0002421692,0.0001555273],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002889109,0.0003590852,0.02993541,0.0003551448,0.00006559105,0.00002936597,0.04194691,0.000331647,0.002605095,0.0008435716,0.006513325,0.9167259],"study_design_scores_gemma":[0.0001598572,0.001725084,0.009505231,0.003629318,0.000694306,0.00006748013,0.1873226,0.1287882,0.2783732,0.007635082,0.3801516,0.001948065],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9557766,0.006814558,0.0151107,0.01236211,0.005840735,0.003447954,0.00002841741,0.0003170619,0.000301871],"genre_scores_gemma":[0.9893996,0.0002854191,0.0006305729,0.0008195228,0.002066617,0.005615436,0.000006691434,0.00004519185,0.001131023],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9147779,"threshold_uncertainty_score":0.9999895,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.275358555671222,"score_gpt":0.546426657391129,"score_spread":0.2710681017199069,"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."}}