{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004052998,0.0002776793,0.0003174141,0.001096083,0.0004939074,0.000398634,0.0002765878,0.0002076932,0.00001094451],"category_scores_gemma":[0.00003778167,0.0002840802,0.00008356349,0.001649508,0.0001749768,0.0006499225,0.00002359511,0.0001638769,0.000001785134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004930074,"about_ca_system_score_gemma":0.0001595285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000372779,"about_ca_topic_score_gemma":0.00009233327,"domain_scores_codex":[0.9979954,0.0001824532,0.0005699291,0.0006534202,0.0003010609,0.000297729],"domain_scores_gemma":[0.9986933,0.0004009933,0.0001418668,0.0003521916,0.0002524194,0.0001592435],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006883132,0.001533176,0.0009653221,0.0004770675,0.0001775957,0.0000101664,0.002962893,0.002051081,0.0003092922,0.9658017,0.00354111,0.02210173],"study_design_scores_gemma":[0.001171757,0.0001428777,0.0003463819,0.0002204352,0.00005396204,0.00001092497,0.0001308041,0.991949,0.000616993,0.001241786,0.003830056,0.0002850126],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007881455,0.00004280822,0.9903583,0.0005275217,0.0004979699,0.000455806,0.00004742857,0.000155064,0.00003363631],"genre_scores_gemma":[0.9798326,0.000970585,0.008799964,0.009835439,0.00007941879,0.00004824697,0.0001200939,0.00004076019,0.0002728515],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9898979,"threshold_uncertainty_score":0.9999611,"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."}}