{"id":"W2911605224","doi":"10.3322/caac.21552","title":"Artificial intelligence in cancer imaging: Clinical challenges and applications","year":2019,"lang":"en","type":"review","venue":"CA A Cancer Journal for Clinicians","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1829,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Medical Research Council; Natural Sciences and Engineering Research Council of Canada; Novo Nordisk Fonden; National Institute for Health and Care Research; National Institute of Biomedical Imaging and Bioengineering; Wellcome Trust; Francis Crick Institute; National Cancer Institute; National Institutes of Health; Cancer Research UK; AstraZeneca","keywords":"Medicine; Context (archaeology); Workflow; Generalizability theory; Medical physics; Precision medicine; Disease; Medical imaging; Artificial intelligence; Pathology; Radiology; Computer science; Psychology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2566280228861588,"score_gpt":0.5461063273453626,"score_spread":0.2894783044592038,"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."}}