{"id":"W4381309178","doi":"10.1001/jamaneurol.2023.1645","title":"Automated Interpretation of Clinical Electroencephalograms Using Artificial Intelligence","year":2023,"lang":"en","type":"article","venue":"JAMA Neurology","topic":"EEG and Brain-Computer Interfaces","field":"Neuroscience","cited_by":218,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Toronto Western Hospital; Hospital for Sick Children; University of Toronto","funders":"","keywords":"Interpretation (philosophy); Artificial intelligence; Electroencephalography; Psychology; Computer science; Neuroscience","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":[0.000504531,0.000125001,0.0002936268,0.0002025442,0.00006151033,0.0000414843,0.0004088063,0.0001947935,0.00002402163],"category_scores_gemma":[0.001552561,0.0001151729,0.0001112759,0.0007298136,0.0003105752,0.0001556767,0.0001497628,0.0004515405,0.0001090541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004820941,"about_ca_system_score_gemma":0.00004352105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001722308,"about_ca_topic_score_gemma":0.000007588806,"domain_scores_codex":[0.9976429,0.0006067507,0.0007309393,0.0004866706,0.0001777408,0.000355047],"domain_scores_gemma":[0.9981426,0.001257213,0.000255955,0.0002312569,0.00005273846,0.00006023475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000963096,0.0003085684,0.003451269,0.00004322437,0.00002052705,0.000303111,0.0007772317,0.01749273,0.7004269,0.01215068,0.0008368429,0.2632259],"study_design_scores_gemma":[0.00006641389,0.0007053408,0.002296655,0.000008521713,0.00000641115,0.00006309179,0.00001327082,0.8481282,0.143078,0.005391992,0.0001608634,0.00008125199],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98752,0.000005345758,0.009022292,0.001130476,0.001321232,0.000135835,0.000005054546,0.0006090932,0.0002506431],"genre_scores_gemma":[0.9977307,0.00001852948,0.0002702789,0.001772845,0.0001761498,0.000004512549,0.00000270807,0.00001520838,0.000009076046],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8306354,"threshold_uncertainty_score":0.4696616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1040632846233787,"score_gpt":0.395315310662682,"score_spread":0.2912520260393033,"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."}}