{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0515666,0.00178276,0.001453849,0.003814297,0.009793772,0.03903939,0.006243829,0.01866679,0.01227313],"category_scores_gemma":[0.02908958,0.001055176,0.002157569,0.002471286,0.06256223,0.0457202,0.02632002,0.02684096,0.005693263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01089339,"about_ca_system_score_gemma":0.02808196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004363523,"about_ca_topic_score_gemma":0.003510468,"domain_scores_codex":[0.9676415,0.01985611,0.001215841,0.002923211,0.005722804,0.002640488],"domain_scores_gemma":[0.9599487,0.01591002,0.002018074,0.006220247,0.005471764,0.01043107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002035664,0.00005459692,0.0002768866,0.0001995854,0.00002338947,0.00007068722,0.003104277,0.0006081701,0.000161094,0.90745,0.05156435,0.03646673],"study_design_scores_gemma":[0.00001563367,0.00003317271,0.0001306507,0.0003749392,0.000009140792,0.0000979937,0.002080045,0.0008292683,0.0001381636,0.6689707,0.3272855,0.00003469141],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.002412986,0.02192453,0.1537151,0.5904922,0.007092909,0.0002616179,0.000144713,0.001213494,0.2227425],"genre_scores_gemma":[0.3627774,0.04371211,0.3215573,0.1532379,0.01612969,0.001786693,0.0006099153,0.001776746,0.09841228],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0515666,"threshold_uncertainty_score":0.2727135,"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."}}