{"id":"W3111350502","doi":"10.1016/j.jmir.2020.11.013","title":"Perceptions of Canadian radiation oncologists, radiation physicists, radiation therapists and radiation trainees about the impact of artificial intelligence in radiation oncology – national survey","year":2020,"lang":"en","type":"article","venue":"Journal of medical imaging and radiation sciences","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Health Sciences Centre; Sunnybrook Health Science Centre","funders":"Association Canadienne des Technologues en Radiation Médicale; Canadian Association of Radiation Oncology","keywords":"Radiation oncology; Radiation Therapist; Specialty; Medicine; Likert scale; Medical physicist; Medical radiation; Perception; Medical education; Medical physics; Radiation oncologist; Family medicine; Radiation therapy; Psychology; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005767564,0.0001966895,0.0005161579,0.001177106,0.0002927776,0.00008543656,0.0002484409,0.0002207088,0.0001192104],"category_scores_gemma":[0.006015052,0.0001454831,0.0001374964,0.001890202,0.0007073825,0.0006602042,0.00001707179,0.0004877477,0.000003367659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006384893,"about_ca_system_score_gemma":0.004215495,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02440667,"about_ca_topic_score_gemma":0.002656625,"domain_scores_codex":[0.9959189,0.0006997442,0.001540757,0.0003078163,0.001214315,0.0003184146],"domain_scores_gemma":[0.9958947,0.001655196,0.001245007,0.0001047135,0.0006164256,0.0004839822],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0001402883,0.0001523049,0.1398868,0.00004323707,0.00004203527,0.000003183983,0.01130588,0.002766427,0.0006864164,0.0006170316,0.001326042,0.8430304],"study_design_scores_gemma":[0.0003549832,0.0008890494,0.7927902,0.0001056449,0.00005128618,0.00006889588,0.006321631,0.196295,0.0008586993,0.001128059,0.0009766571,0.000159839],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9426159,0.007515669,0.003121324,0.04532744,0.0006928178,0.0005089486,0.0000475418,0.00001137201,0.0001589602],"genre_scores_gemma":[0.9811617,0.0166685,0.0002052123,0.0008415956,0.001028288,0.000009936509,0.00007059006,0.00001129847,0.000002879731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8428705,"threshold_uncertainty_score":0.9820899,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1714278642566501,"score_gpt":0.4879162178932095,"score_spread":0.3164883536365595,"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."}}