{"id":"W4405285388","doi":"10.1080/17435889.2024.2439776","title":"Nanomedicine and clinical diagnostics part I: applications in conventional imaging (MRI, X-ray/CT, and ultrasound)","year":2024,"lang":"en","type":"review","venue":"Nanomedicine","topic":"Nanoplatforms for cancer theranostics","field":"Engineering","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Medical physics; Medical imaging; Nanomedicine; Modalities; Modality (human–computer interaction); Clinical Practice; Clinical imaging; Magnetic resonance imaging; Computer science; Nanotechnology; Medicine; Artificial intelligence; Radiology; Materials science; Nanoparticle","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009167835,0.001002714,0.001294121,0.002843982,0.0004336697,0.001791311,0.000650904,0.001794052,0.00507797],"category_scores_gemma":[0.001201032,0.0003686552,0.0008445857,0.002331433,0.001065764,0.001985656,0.000995625,0.00202783,0.001832999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008663401,"about_ca_system_score_gemma":0.001580083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009857152,"about_ca_topic_score_gemma":0.001207639,"domain_scores_codex":[0.9993646,0.0001363752,0.00009427942,0.0001132506,0.0002399294,0.0000516682],"domain_scores_gemma":[0.9992353,0.0004776374,0.00009665633,0.00002701904,0.0001358299,0.00002772424],"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":[0.00005030372,0.0001144391,0.0002093451,0.04195768,0.0000914535,0.0002043327,0.0001941099,0.0005324474,0.006775881,0.01622841,0.02418396,0.9094576],"study_design_scores_gemma":[0.000006187971,0.0001154692,0.0006037299,0.006126078,0.0000619996,0.001070196,0.00008233007,0.0001136873,0.0016974,0.003284388,0.9868178,0.00002065701],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00016233,0.9959118,0.0004043874,0.0003553348,0.0003786114,0.00001587369,0.00001629839,0.000008698909,0.00274665],"genre_scores_gemma":[0.001453429,0.9958078,0.0005784185,0.0004491497,0.0003455055,0.00002805666,0.0000238583,0.000003064177,0.001310805],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.00507797,"threshold_uncertainty_score":0.01698744,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02303403460601282,"score_gpt":0.3271974200723141,"score_spread":0.3041633854663013,"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."}}