{"id":"W4318485927","doi":"10.3390/curroncol30020125","title":"Artificial Intelligence for Cancer Detection—A Bibliometric Analysis and Avenues for Future Research","year":2023,"lang":"en","type":"review","venue":"Current Oncology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Research Foundation of Korea; Science and Technology Commission of Shanghai Municipality; National Natural Science Foundation of China; Universität Duisburg-Essen; National Cancer Institute; National Key Research and Development Program of China; National Research Foundation; European Regional Development Fund; European Commission; Fundamental Research Funds for the Central Universities; National Institute for Health and Care Research; U.S. Department of Health and Human Services; National Institutes of Health; Japan Society for the Promotion of Science; Cancer Research UK; Canadian Institutes of Health Research; National Science Foundation","keywords":"Artificial intelligence; Cancer; Medical research; Medicine; Data science; Disease; Cancer detection; Computer science; Pathology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.002397392,0.0002167701,0.001642547,0.0446815,0.0002226202,0.00004758642,0.0001738617,0.0003483258,0.0000244091],"category_scores_gemma":[0.002825119,0.0001689381,0.000541879,0.05870386,0.0001933155,0.00002217517,0.00008970518,0.00113408,0.0000114394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003314158,"about_ca_system_score_gemma":0.0005171659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002868269,"about_ca_topic_score_gemma":0.00008935946,"domain_scores_codex":[0.9978718,0.0002138373,0.0006083603,0.0005626937,0.0002772316,0.0004660384],"domain_scores_gemma":[0.9964221,0.002518732,0.0002413356,0.0002193948,0.0003934911,0.0002050198],"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":[0.00002947267,0.00007238358,0.0000490634,0.015031,0.0006647654,0.000004232916,0.00005871385,0.000001030421,5.658977e-7,0.0001038476,0.001034128,0.9829508],"study_design_scores_gemma":[0.00009454786,0.0003451653,0.00006503679,0.001641604,0.00486218,0.00001369153,0.00004985281,0.002873922,0.000001671814,0.0006411594,0.9892715,0.0001396816],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005356731,0.9736797,0.02111198,0.0006571021,0.002688005,0.00164258,0.0001089113,0.00004618728,0.00001192149],"genre_scores_gemma":[0.00002589057,0.9926579,0.0006101357,0.00001828018,0.004516667,0.001776063,0.0002086181,0.00005295,0.0001334932],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9882374,"threshold_uncertainty_score":0.9661462,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4913832157191078,"score_gpt":0.6236155400401707,"score_spread":0.132232324321063,"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."}}