{"id":"W4415601405","doi":"10.14740/aicm5","title":"Generative Artificial Intelligence as a Catalyst for Effective Cancer Treatments","year":2025,"lang":"en","type":"article","venue":"AI in Clinical Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cancer; Immunotherapy; Precision medicine; Disease; Cancer immunotherapy; Cancer therapy; Cancer treatment","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":[],"consensus_categories":[],"category_scores_codex":[0.001194089,0.0001915297,0.0006657559,0.0002358685,0.0001056889,0.000008353991,0.000111552,0.0002438444,0.0001666577],"category_scores_gemma":[0.005864223,0.0001434423,0.0001326471,0.0006076124,0.000360881,0.00005919336,0.00002609999,0.0004235489,0.00006132371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003012469,"about_ca_system_score_gemma":0.0006065106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004282583,"about_ca_topic_score_gemma":0.001479511,"domain_scores_codex":[0.9974365,0.0001483403,0.001346417,0.0005411694,0.0001939851,0.0003335777],"domain_scores_gemma":[0.9958408,0.003106972,0.0001454097,0.0003241219,0.0003928194,0.0001898654],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001346898,0.0006894736,0.04003899,0.0001519066,0.0001744329,0.00001606597,0.001273996,0.00002464246,0.0004035675,0.005456095,0.003546161,0.9468778],"study_design_scores_gemma":[0.002411681,0.02055679,0.1615665,0.009272161,0.002623215,0.00003084413,0.01186016,0.01884742,0.1495596,0.5918793,0.03032757,0.001064691],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7569112,0.002282324,0.01551495,0.2114631,0.006198348,0.005118543,0.00001499802,0.00007847643,0.00241816],"genre_scores_gemma":[0.9783702,0.0007562576,0.0005251114,0.01608434,0.001649447,0.001259212,0.00005930857,0.00001614778,0.001279948],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9458131,"threshold_uncertainty_score":0.7020447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3035080272125203,"score_gpt":0.6123925469580297,"score_spread":0.3088845197455094,"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."}}