{"id":"W4413419002","doi":"10.1182/blood.2025029876","title":"Artificial intelligence in hematology","year":2025,"lang":"en","type":"article","venue":"Blood","topic":"AI in cancer detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Jewish General Hospital","funders":"U.S. Food and Drug Administration; National Cancer Institute; National Institutes of Health; Incyte; Swedish Orphan Biovitrum; CSL Behring; Novo Nordisk; Sanofi; Genentech; AstraZeneca","keywords":"Workflow; Artificial intelligence; Personalized medicine; Pace; Medical physics; Computer science; Medicine; Data science; Bioinformatics","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":[0.0001115646,0.00004485269,0.00007227618,0.0001279905,0.00002634352,0.00002608301,0.0003539621,0.00004265302,0.000006515582],"category_scores_gemma":[0.00003340672,0.00004757706,0.00001659273,0.0005505895,0.00002127082,0.00009415016,0.0001129744,0.00008433528,0.0000436372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002042764,"about_ca_system_score_gemma":0.00005448477,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002661504,"about_ca_topic_score_gemma":0.0001070543,"domain_scores_codex":[0.9994532,0.00002638631,0.000144658,0.0001948301,0.00005690222,0.000123954],"domain_scores_gemma":[0.999652,0.00005045602,0.00002132246,0.0002473434,0.00001595474,0.00001296547],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003621824,0.0001763687,0.002260966,0.00001430976,0.000008447787,0.00002486415,0.0002102072,0.00008449442,0.0007844224,0.7788781,0.0000973249,0.2174569],"study_design_scores_gemma":[0.0001451669,0.00007411396,0.002882223,0.00005715475,0.000007668015,0.0000409071,0.00004480134,0.02692909,0.4843425,0.4839346,0.001365823,0.0001760005],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03404719,0.000217395,0.9580234,0.001265184,0.0006119553,0.00007823567,1.478036e-7,0.0001032517,0.005653189],"genre_scores_gemma":[0.9875218,0.000007134016,0.01219466,0.0001833124,0.00002111544,0.00001686392,8.747816e-8,0.000001617632,0.00005345728],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9534746,"threshold_uncertainty_score":0.1940136,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01764554827787898,"score_gpt":0.2766017917018357,"score_spread":0.2589562434239568,"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."}}