{"id":"W4392157110","doi":"10.1002/cyto.b.22166","title":"Recommendations for using artificial intelligence in clinical flow cytometry","year":2024,"lang":"en","type":"review","venue":"Cytometry Part B Clinical Cytometry","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Children's Hospital; University of British Columbia","funders":"","keywords":"Computer science; Multidisciplinary approach; Identification (biology); Medical physics; Cytometry; Artificial intelligence; Data science; Flow cytometry; Medicine; Immunology; Biology","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":[0.01175041,0.001267722,0.002036674,0.004947687,0.0005981532,0.003486723,0.004162171,0.006378833,0.01838524],"category_scores_gemma":[0.03057739,0.0007780445,0.002802664,0.004577254,0.001617189,0.004807806,0.001996902,0.008553861,0.01711825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002962566,"about_ca_system_score_gemma":0.009553839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005516053,"about_ca_topic_score_gemma":0.01069468,"domain_scores_codex":[0.9943757,0.002304144,0.001110503,0.0003620535,0.001624181,0.0002234628],"domain_scores_gemma":[0.9749305,0.01290512,0.001934605,0.0006838665,0.008627048,0.0009188372],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006515412,0.00009126748,0.0001660822,0.02415566,0.0001222394,0.000208209,0.0001603329,0.0004361554,0.000388483,0.01349391,0.4335284,0.5271841],"study_design_scores_gemma":[0.00004075007,0.00002737273,0.0002314427,0.01898844,0.00009794499,0.0001573577,0.0000632688,0.0000693868,0.00007049293,0.006222498,0.9740084,0.0000226989],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001860613,0.844565,0.004668467,0.1162769,0.01437511,0.0005388542,0.001115596,0.0002959897,0.01797798],"genre_scores_gemma":[0.001204309,0.920459,0.01884941,0.04378161,0.002729648,0.0008396741,0.001017135,0.00006783071,0.01105142],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01838524,"threshold_uncertainty_score":0.06214285,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3952808734457134,"score_gpt":0.5153069385140041,"score_spread":0.1200260650682907,"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."}}