{"id":"W2938914726","doi":"10.2196/11472","title":"A Facial Recognition Mobile App for Patient Safety and Biometric Identification: Design, Development, and Validation","year":2019,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":48,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Seoul National University Hospital; National Research Foundation of Korea; Korea Health Industry Development Institute; Seoul National University; National Research Foundation","keywords":"Identification (biology); Patient safety; Biometrics; Medicine; Health care; Medical emergency; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.002617194,0.0009050257,0.0005279379,0.000747797,0.0003917205,0.0007604919,0.001190945,0.0009440585,0.003478206],"category_scores_gemma":[0.003404827,0.0003417704,0.0005533266,0.0002058711,0.000410883,0.001016585,0.0009167988,0.0006868849,0.001148895],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003386175,"about_ca_system_score_gemma":0.001113851,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001169294,"about_ca_topic_score_gemma":0.00111773,"domain_scores_codex":[0.9984263,0.000381993,0.0001607831,0.0001799379,0.0007047368,0.0001462171],"domain_scores_gemma":[0.997852,0.0006057973,0.0001122572,0.00009740582,0.001208949,0.0001236302],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.003928288,0.006949117,0.028228,0.00657852,0.0003081737,0.00484271,0.004945316,0.00523295,0.1902656,0.003881751,0.02035844,0.7244812],"study_design_scores_gemma":[0.002595291,0.05939716,0.1598765,0.003658091,0.002182611,0.01889204,0.005288223,0.1660333,0.4247157,0.002776653,0.1535506,0.001033995],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7364943,0.003049368,0.2063333,0.0007910759,0.0006068531,0.03353264,0.00121572,0.005053212,0.01292356],"genre_scores_gemma":[0.6027489,0.00293642,0.34977,0.001000488,0.0001142592,0.01968534,0.001544851,0.0003458132,0.02185392],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003478206,"threshold_uncertainty_score":0.01384121,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07627390721603412,"score_gpt":0.3389561442877376,"score_spread":0.2626822370717035,"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."}}