{"id":"W4406417577","doi":"10.1186/s12910-024-01158-1","title":"High-reward, high-risk technologies? An ethical and legal account of AI development in healthcare","year":2025,"lang":"en","type":"review","venue":"BMC Medical Ethics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute; HEC Montréal; Université de Montréal","funders":"HEC Montréal; Institut de Valorisation des Données; Fonds de Recherche du Québec-Société et Culture; Canadian Institute for Advanced Research","keywords":"Philosophy of medicine; Health care; Medical law; Engineering ethics; Psychology; Political science; Medicine; Law; Alternative medicine; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","research_integrity"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.006861051,0.0004272805,0.002208934,0.0005850162,0.0001677625,0.00002418335,0.0004283802,0.008148118,0.00005617528],"category_scores_gemma":[0.06189469,0.000332993,0.0001492793,0.0008060524,0.0006775237,0.00006254829,0.0002422922,0.0147245,0.00001897532],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004623753,"about_ca_system_score_gemma":0.06732342,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.01376386,"about_ca_topic_score_gemma":0.04298857,"domain_scores_codex":[0.9936292,0.00126108,0.002176371,0.0007642171,0.001640355,0.0005287414],"domain_scores_gemma":[0.9854899,0.01202518,0.0005340857,0.0007809756,0.0006765841,0.000493281],"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":[0.00004132823,0.000157997,0.0005764103,0.1236014,0.00003454073,0.00003510509,0.0004106397,3.465351e-7,2.640309e-8,0.01971145,0.0002579771,0.8551728],"study_design_scores_gemma":[0.000289349,0.000755221,0.0002826936,0.2164104,0.0006188559,0.0001406343,0.001485384,0.0000315732,0.00005640308,0.008039211,0.77116,0.0007302844],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001042887,0.9510043,0.00134505,0.04384413,0.001227606,0.001333704,0.00003093068,0.0001556873,0.00001573667],"genre_scores_gemma":[0.002882541,0.9870853,0.005415337,0.003767637,0.0002741324,0.0002272086,0.0002506974,0.00003574891,0.0000614073],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.8544425,"threshold_uncertainty_score":0.9999122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2178461611692171,"score_gpt":0.5121835296914428,"score_spread":0.2943373685222257,"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."}}