{"id":"W2886522935","doi":"10.1111/joim.12822","title":"<scp>eD</scp>octor: machine learning and the future of medicine","year":2018,"lang":"en","type":"review","venue":"Journal of Internal Medicine","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":923,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Medicine; Personalized medicine; Field (mathematics); Alternative medicine; Precision medicine; MEDLINE; Artificial intelligence; Health care; Data science; Medical education; Bioinformatics; Computer science; Pathology","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":["metaresearch","metaepi_narrow","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008703981,0.0005708727,0.004402346,0.00064001,0.0004253598,0.000005866803,0.001014919,0.0006641957,0.001083008],"category_scores_gemma":[0.01209126,0.0002469553,0.0004412666,0.0004219262,0.001966414,0.0001028491,0.0002803102,0.006237649,0.00006392114],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002814078,"about_ca_system_score_gemma":0.0006410233,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001146137,"about_ca_topic_score_gemma":0.0002962622,"domain_scores_codex":[0.9895264,0.003218926,0.005208962,0.0003122152,0.001169248,0.0005642657],"domain_scores_gemma":[0.9780545,0.0111508,0.008298128,0.0004007356,0.001653295,0.0004425469],"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":[0.0003574207,0.00004595206,0.002878504,0.03674939,0.0009694313,0.0001906745,0.01762759,3.516004e-7,0.000003518584,0.002510253,0.1517726,0.7868943],"study_design_scores_gemma":[0.001006917,0.001416622,0.00001377254,0.1297436,0.0009613446,0.0005492248,0.007947632,0.00003596518,7.732346e-7,0.0006904564,0.8575656,0.00006814796],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0001518341,0.9817209,0.0001554938,0.004411453,0.009219706,0.0008741864,0.00001313894,0.0000174224,0.003435812],"genre_scores_gemma":[0.0002764045,0.9683216,0.00008773665,0.00100357,0.02740137,0.00001857771,0.000007798182,0.00008164649,0.002801249],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.7868261,"threshold_uncertainty_score":0.9999983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1414722402719778,"score_gpt":0.5221836019344029,"score_spread":0.3807113616624251,"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."}}