{"id":"W4313594376","doi":"10.1016/j.ebiom.2022.104427","title":"Computational pathology in 2030: a Delphi study forecasting the role of AI in pathology within the next decade","year":2023,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); Canada's Michael Smith Genome Sciences Centre; University of British Columbia","funders":"UK Research and Innovation; Nuclear Power Institute of China","keywords":"Workflow; Delphi method; Likert scale; Medical laboratory; Pathology; Medicine; Subject-matter expert; Delphi; Workforce; Surgical pathology; Medical physics; Computer science; Data science; Artificial intelligence; Psychology; Expert system","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":[0.002069973,0.000100983,0.0002721492,0.0003659552,0.00008164507,0.000004837034,0.0001387099,0.00008447246,0.00002712645],"category_scores_gemma":[0.0008393734,0.00005793795,0.00002820203,0.001335502,0.0002661528,0.00004087801,0.00005114795,0.0004360929,0.00002720232],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005725986,"about_ca_system_score_gemma":0.0002202295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001340394,"about_ca_topic_score_gemma":0.001652566,"domain_scores_codex":[0.9983491,0.0002294384,0.0007015761,0.0002149451,0.000245303,0.0002596691],"domain_scores_gemma":[0.9986284,0.0008369463,0.0001354267,0.0002264939,0.0001248728,0.00004787013],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002446389,0.0005201031,0.7801108,0.00006017844,0.0000229907,0.0004234841,0.1132932,0.00268931,0.001922816,0.0006806038,0.0005191444,0.0995127],"study_design_scores_gemma":[0.0005838881,0.001450557,0.69108,0.000239181,0.00004549795,0.0003498017,0.1901926,0.08855709,0.0005587079,0.02649948,0.0003302584,0.0001130462],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9781177,0.0003346772,0.00009034828,0.02019541,0.0003774417,0.0007523148,0.000001823497,0.00002223327,0.0001080521],"genre_scores_gemma":[0.9980399,0.00001651363,0.00007917648,0.00143938,0.0002521314,0.00009185627,0.00003412851,0.00001204793,0.00003485047],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09939966,"threshold_uncertainty_score":0.2362641,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2012085920482579,"score_gpt":0.4395051646988165,"score_spread":0.2382965726505586,"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."}}