{"id":"W4414282958","doi":"10.1016/j.lmd.2025.100095","title":"The digital transformation of laboratory medicine: From instruments to intelligence","year":2025,"lang":"en","type":"article","venue":"LabMed discovery.","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto; Hospital for Sick Children","funders":"Hospital for Sick Children","keywords":"Digital transformation; Transformation (genetics); Component (thermodynamics); Identification (biology); Automation","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002524453,0.0004190562,0.000351485,0.002300598,0.0004744518,0.007836928,0.0009836935,0.00114427,0.005432819],"category_scores_gemma":[0.009265685,0.0002562304,0.0004135029,0.00181317,0.006359809,0.006765024,0.003125652,0.002104751,0.001728265],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233678,"about_ca_system_score_gemma":0.001905881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009363305,"about_ca_topic_score_gemma":0.0006416973,"domain_scores_codex":[0.9979764,0.0009238454,0.0001172686,0.0001615408,0.0007301917,0.00009070347],"domain_scores_gemma":[0.9943845,0.002981575,0.000327394,0.001420012,0.0006336689,0.0002528028],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001288183,0.00008573673,0.002089338,0.0005827508,0.00003686999,0.000217553,0.001071777,0.002819805,0.003576139,0.4925241,0.01959958,0.4772675],"study_design_scores_gemma":[0.00003786009,0.0001218903,0.002378976,0.0009647573,0.00003201274,0.0008568046,0.001366399,0.008456004,0.00769628,0.575116,0.4029067,0.00006624508],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.04134728,0.05791433,0.4702831,0.07567668,0.003814133,0.0002310555,0.001062,0.003359649,0.3463118],"genre_scores_gemma":[0.6668096,0.05193592,0.2304263,0.008784845,0.003151125,0.0001836807,0.0007805051,0.0004000781,0.03752787],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007836928,"threshold_uncertainty_score":0.01817459,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04445477283538416,"score_gpt":0.3734869744383345,"score_spread":0.3290322016029503,"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."}}