{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01392402,0.000599509,0.0006787216,0.001236076,0.001391452,0.002312331,0.001102546,0.001522991,0.002538792],"category_scores_gemma":[0.03366948,0.0004400536,0.0006015036,0.0008311202,0.001252036,0.001800462,0.002679577,0.001046671,0.000504141],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008354126,"about_ca_system_score_gemma":0.0009768766,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006715514,"about_ca_topic_score_gemma":0.0009120257,"domain_scores_codex":[0.9906024,0.007142243,0.0003598677,0.0004062619,0.0007812695,0.0007080511],"domain_scores_gemma":[0.965727,0.0267217,0.001191096,0.001270492,0.00306934,0.002020389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0009160053,0.004481382,0.08757779,0.002074476,0.0001669904,0.003804266,0.7361543,0.001621791,0.006354533,0.001230203,0.007215626,0.1484027],"study_design_scores_gemma":[0.0003923378,0.01904069,0.1374469,0.001269059,0.0003254803,0.01352761,0.7332616,0.02048644,0.008549027,0.001670866,0.06359176,0.0004381713],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934207,0.0002778817,0.003200922,0.0004432358,0.00001339169,0.0002601604,0.00005314755,0.00005895349,0.002271667],"genre_scores_gemma":[0.9914067,0.0004267239,0.006075285,0.000434114,0.00001725667,0.0003213953,0.000061081,0.00003697761,0.001220481],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01392402,"threshold_uncertainty_score":0.07363814,"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."}}