{"id":"W4410183135","doi":"10.3389/fdgth.2025.1600216","title":"AI with agency: a vision for adaptive, efficient, and ethical healthcare","year":2025,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Multimedia University","keywords":"Agency (philosophy); Health care; Engineering ethics; Computer science; Sociology; Psychology; Cognitive science; Artificial intelligence; Business; Environmental ethics; Political science; Engineering; Philosophy; Social science; Law","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0003738334,0.0001326676,0.0003488465,0.000273914,0.000164768,0.00004032631,0.00005401794,0.0001937216,0.000001372963],"category_scores_gemma":[0.0001838642,0.0001112759,0.00003814571,0.0004319934,0.0001363948,0.00007384885,0.00002081756,0.0004684816,0.000001851431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003548905,"about_ca_system_score_gemma":0.001371431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003862868,"about_ca_topic_score_gemma":0.0001680805,"domain_scores_codex":[0.9985892,0.00003290346,0.0004310736,0.0003694085,0.000165802,0.0004116458],"domain_scores_gemma":[0.9991835,0.0001321181,0.00007380577,0.0001883464,0.0001808204,0.0002413908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002392046,0.0002857128,0.1319413,0.001773515,0.00002361492,0.000006378503,0.003011301,0.00003285368,0.000001744367,0.009661091,0.04207763,0.8087928],"study_design_scores_gemma":[0.005434417,0.03545447,0.3156245,0.0198219,0.0001540987,0.000165281,0.04476831,0.1591444,0.0007398285,0.2985545,0.1180935,0.002044772],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4210238,0.008267357,0.2625993,0.2993229,0.002155395,0.004730922,0.00009124984,0.0001492307,0.001659855],"genre_scores_gemma":[0.9792916,0.0001640259,0.006820182,0.0131847,0.00007088718,0.00008481482,0.00004569119,0.00001530554,0.000322856],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.806748,"threshold_uncertainty_score":0.4537698,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05221097324674016,"score_gpt":0.4202799886591826,"score_spread":0.3680690154124424,"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."}}