{"id":"W4417002795","doi":"10.1109/tvcg.2025.3633883","title":"“It Looks Sexy but it's Wrong.” Tensions in Creativity and Accuracy using genAI for Biomedical Visualization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Trond Mohn stiftelse","keywords":"Visualization; Workflow; Creativity; Pipeline (software); Scientific visualization; Data visualization; Information visualization; Creative visualization; Embodied cognition","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03542264,0.0008266758,0.0003754053,0.002654243,0.003979822,0.01014015,0.002078155,0.002466318,0.01058904],"category_scores_gemma":[0.08508384,0.0005969353,0.001073713,0.00168256,0.01518308,0.009343303,0.007715284,0.003724406,0.002292658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002516225,"about_ca_system_score_gemma":0.002469458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001441348,"about_ca_topic_score_gemma":0.003153485,"domain_scores_codex":[0.980376,0.01516566,0.0005020773,0.0009801926,0.002403218,0.0005728929],"domain_scores_gemma":[0.9092369,0.07309356,0.003368013,0.008574503,0.004030349,0.001696668],"domain_codex":null,"domain_gemma":"reporting","domain_candidate":"reporting","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001645323,0.00008040458,0.01403771,0.001273055,0.00007195148,0.001338854,0.6257296,0.001021054,0.008223668,0.178126,0.02635328,0.1435799],"study_design_scores_gemma":[0.00006094494,0.0001686212,0.00982908,0.001826257,0.0001108501,0.003581456,0.2013173,0.005067675,0.007904798,0.1890455,0.5808396,0.0002477636],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2707162,0.005044959,0.4199026,0.07211583,0.001712986,0.0005347375,0.0008522685,0.004644377,0.2244761],"genre_scores_gemma":[0.8201934,0.002002287,0.1488973,0.007052604,0.0002391605,0.0005111046,0.0004227426,0.001865222,0.01881628],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9645774,"threshold_uncertainty_score":0.1873351,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04428523211648161,"score_gpt":0.3552425932021934,"score_spread":0.3109573610857118,"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."}}