{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.01434662,0.0007606619,0.002668077,0.05484307,0.0008554897,0.005727511,0.001084241,0.000801661,0.004611069],"category_scores_gemma":[0.03613056,0.0004279661,0.002163754,0.08594821,0.001032916,0.006181303,0.001317604,0.001178355,0.0007247298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00349323,"about_ca_system_score_gemma":0.007671009,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004813769,"about_ca_topic_score_gemma":0.008752538,"domain_scores_codex":[0.9919666,0.002723223,0.00145895,0.0005101082,0.003085249,0.0002558272],"domain_scores_gemma":[0.9554175,0.03091322,0.004614925,0.0008959793,0.007503005,0.0006553222],"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":[0.0001267174,0.0001034172,0.01865925,0.08849534,0.002357891,0.0002287172,0.0008881737,0.0007925917,0.0003601937,0.01603843,0.03329952,0.8386497],"study_design_scores_gemma":[0.00006412943,0.0002422836,0.08449325,0.1570306,0.007470565,0.001510639,0.004497735,0.003822922,0.0008336474,0.03847179,0.7013221,0.0002403392],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.003805354,0.9811322,0.001233009,0.007817027,0.0004494581,0.0001005547,0.0009323852,0.00004151625,0.004488425],"genre_scores_gemma":[0.03037875,0.9636882,0.003049711,0.0008555812,0.0006888491,0.0001058047,0.0007865977,0.00001695748,0.0004295745],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9451569,"threshold_uncertainty_score":0.07587308,"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."}}