{"id":"W4415688800","doi":"10.1007/978-3-032-04339-9_17","title":"Investigating Zero-Shot Diagnostic Pathology in Vision-Language Models with Efficient Prompt Design","year":2025,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; Université de Montréal; Concordia University","funders":"","keywords":"Context (archaeology); Domain (mathematical analysis); Task (project management); Digital pathology; Computational model; Clinical Practice; Computational Science and Engineering","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":[],"consensus_categories":[],"category_scores_codex":[0.002780072,0.0008016637,0.000985541,0.0004070002,0.0004485591,0.002234477,0.002124306,0.00182908,0.006106935],"category_scores_gemma":[0.01239542,0.00081626,0.0009557907,0.000378236,0.001018691,0.00330095,0.001713382,0.002531005,0.001193811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001490701,"about_ca_system_score_gemma":0.00216514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005101251,"about_ca_topic_score_gemma":0.006155756,"domain_scores_codex":[0.9989792,0.0004214519,0.0000381043,0.0002577788,0.0001857012,0.0001177853],"domain_scores_gemma":[0.9923881,0.006117439,0.0002169123,0.0005215002,0.000582864,0.0001730882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001234354,0.0004209357,0.002710843,0.0004753326,0.0001303097,0.0003491027,0.0007346413,0.4835674,0.02341611,0.07307018,0.006987903,0.4069029],"study_design_scores_gemma":[0.00001700846,0.00007273068,0.0001247101,0.00000876324,0.00001600362,0.00003990332,0.00005194636,0.9677503,0.004860449,0.02654263,0.0005063377,0.000009196698],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0354453,0.0002346897,0.9592111,0.0004871098,0.00005561502,0.00006536461,0.0001519651,0.002497192,0.001851661],"genre_scores_gemma":[0.6467915,0.0001992145,0.3451903,0.0003659017,0.00004649294,0.0001161608,0.0005091322,0.0005363805,0.006245114],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006106935,"threshold_uncertainty_score":0.02042973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0419747694207174,"score_gpt":0.295727371560896,"score_spread":0.2537526021401786,"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."}}