{"id":"W4415601427","doi":"10.14740/aicm9","title":"Benefits of AI in Transforming Cancer Care","year":2025,"lang":"en","type":"article","venue":"AI in Clinical Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"","keywords":"Precision medicine; Cancer; Transformative learning; Clinical trial; Quality of life (healthcare); Health care; Wearable technology; Patient care; MEDLINE","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":[0.0008153757,0.00009829335,0.0005444007,0.0002670192,0.00001987303,0.000001352189,0.00008888728,0.0002116147,0.0001209826],"category_scores_gemma":[0.001552177,0.00007817033,0.00006179246,0.0006445708,0.0001828454,0.00004676805,0.00001229361,0.0006521345,0.000004886584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001197024,"about_ca_system_score_gemma":0.0005444225,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008792777,"about_ca_topic_score_gemma":0.009268542,"domain_scores_codex":[0.9978505,0.00006331487,0.001432905,0.000248388,0.0001844865,0.0002204009],"domain_scores_gemma":[0.9985012,0.0008632398,0.00007604199,0.000221036,0.0002418634,0.00009657532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001789667,0.0001165574,0.5495955,0.0003714943,0.000009254527,0.000003485666,0.002465524,0.00003281925,0.00006413684,0.0006259706,0.0007903716,0.445746],"study_design_scores_gemma":[0.001033894,0.001061587,0.9620623,0.01036576,0.000116026,0.000002174473,0.008473193,0.0003070833,0.004928752,0.003902299,0.007612389,0.0001345192],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8907042,0.005270516,0.00007224494,0.0999198,0.001472246,0.0004427482,0.000001808179,0.0000126505,0.002103769],"genre_scores_gemma":[0.9835882,0.002497558,0.00004865727,0.0131842,0.0004438037,0.0000441903,0.00001108767,0.000007716193,0.0001745716],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4456114,"threshold_uncertainty_score":0.9978077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2678664382993822,"score_gpt":0.5869836078935813,"score_spread":0.3191171695941991,"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."}}