{"id":"W4323034586","doi":"10.1002/9781119790686.ch36","title":"Medical Device AI Regulatory Expectations","year":2023,"lang":"en","type":"other","venue":"AI in Clinical Medicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Food and drug administration; Medical software; Software; European union; Action plan; Action (physics); Plan (archaeology); Medical device; Computer science; Engineering management; Software engineering; Risk analysis (engineering); Business; Engineering; Software development; Software quality; Management; Biomedical engineering; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","research_integrity","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001920701,0.0002811445,0.001060855,0.0005638049,0.00004213741,0.000005003714,0.0002336787,0.001349064,0.008794389],"category_scores_gemma":[0.01506554,0.0002236917,0.0001532232,0.0007150616,0.0006451666,0.00003394805,0.0000496223,0.001841539,0.002342268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001342287,"about_ca_system_score_gemma":0.001115205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003906413,"about_ca_topic_score_gemma":0.01095355,"domain_scores_codex":[0.9957363,0.0002229455,0.00197105,0.0006484435,0.001000485,0.0004207681],"domain_scores_gemma":[0.9952446,0.002741385,0.0003188229,0.0007906159,0.0001874166,0.00071717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004202133,0.0002062308,0.02427412,0.000209127,0.00005767823,0.0001589717,0.0003700245,1.46704e-7,7.019489e-7,0.0003546569,0.8683892,0.1059371],"study_design_scores_gemma":[0.0005997922,0.0009470558,0.02955575,0.01181743,0.0002400655,0.00004360902,0.002276663,0.00026222,0.00001099873,0.00208233,0.9517815,0.0003826033],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.004910993,0.009055714,0.000399918,0.5602802,0.02572376,0.002722182,0.0000161748,0.002271464,0.3946196],"genre_scores_gemma":[0.1089657,0.01200701,0.0004195695,0.1139877,0.04021499,0.0004139485,0.0003491459,0.002271234,0.7213708],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.4462925,"threshold_uncertainty_score":0.9999474,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3289150751988315,"score_gpt":0.5911065299001396,"score_spread":0.2621914547013081,"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."}}