{"id":"W4412347970","doi":"10.2196/67552","title":"Regulatory Insights From 27 Years of Artificial Intelligence/Machine Learning–Enabled Medical Device Recalls in the United States: Implications for Future Governance","year":2025,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Recall; Clearance; Artificial intelligence; Medicine; Precision and recall; Computer science; Mobile device; Psychology; World Wide Web; Cognitive psychology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.02085038,0.0002479106,0.0004543802,0.003507085,0.001798795,0.004872554,0.001334769,0.001981085,0.00340937],"category_scores_gemma":[0.05142667,0.0003811433,0.0008792107,0.00483503,0.002519843,0.003214653,0.002664337,0.003521629,0.000263296],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006161468,"about_ca_system_score_gemma":0.01360967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05232655,"about_ca_topic_score_gemma":0.05747995,"domain_scores_codex":[0.988875,0.003026348,0.001472895,0.001915308,0.003631351,0.001078944],"domain_scores_gemma":[0.9206514,0.03331345,0.02685349,0.004019006,0.01177141,0.003391328],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000321535,0.0006179404,0.5754426,0.000773379,0.0003066316,0.0008612539,0.007697819,0.002419142,0.0005786783,0.08546491,0.08327439,0.2422418],"study_design_scores_gemma":[0.00006539829,0.0003365913,0.7117587,0.003504197,0.0002308505,0.0007141026,0.01056336,0.002664662,0.0008428491,0.03123251,0.2379075,0.000179315],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5062839,0.05674513,0.01187325,0.2947995,0.001754871,0.0003741862,0.008688739,0.000249887,0.1192306],"genre_scores_gemma":[0.9220791,0.02737717,0.003043736,0.03799125,0.001449729,0.000194199,0.00321752,0.00007819167,0.004569051],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9791496,"threshold_uncertainty_score":0.1102687,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06344213314089257,"score_gpt":0.3998564817666094,"score_spread":0.3364143486257168,"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."}}