{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04517119,0.0009860825,0.0007811394,0.003144889,0.003320764,0.01536572,0.004358178,0.01558179,0.03614571],"category_scores_gemma":[0.09003484,0.0006170439,0.001585261,0.001648069,0.004568932,0.008892523,0.003952679,0.01579761,0.02209223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.007592338,"about_ca_system_score_gemma":0.02065495,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01309953,"about_ca_topic_score_gemma":0.008699258,"domain_scores_codex":[0.9579944,0.006727189,0.002479123,0.002248209,0.02850789,0.002043233],"domain_scores_gemma":[0.9131854,0.02603439,0.002763515,0.002639328,0.05136168,0.004015724],"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":[0.00009068986,0.0002337839,0.0007367674,0.0006716563,0.00001242234,0.000304058,0.0006703738,0.001068846,0.001347918,0.3865803,0.4964147,0.1118685],"study_design_scores_gemma":[0.00002104132,0.000076189,0.0006555425,0.001320501,0.00001171248,0.0004289952,0.0003254166,0.0005326091,0.0006469669,0.02503928,0.9709047,0.00003692857],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002085658,0.01714416,0.01237996,0.289838,0.007923534,0.0002294216,0.001068103,0.0006444079,0.6686867],"genre_scores_gemma":[0.0891885,0.04892821,0.04047585,0.5132899,0.01039241,0.001374577,0.005634437,0.001241878,0.2894742],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.04517119,"threshold_uncertainty_score":0.2388909,"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."}}