{"id":"W4404414807","doi":"10.1093/jamiaopen/ooae108","title":"Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process","year":2024,"lang":"en","type":"article","venue":"JAMIA Open","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"U.S. National Library of Medicine; National Human Genome Research Institute; National Institutes of Health","keywords":"Deliberation; System lifecycle; Context (archaeology); Process (computing); Engineering ethics; Knowledge management; Health care; Application lifecycle management; Management science; Computer science; Engineering; Political science; Politics","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":[0.0009744541,0.0001762649,0.0003607665,0.0002720516,0.0001765441,0.0003970256,0.0002694174,0.0004072489,0.0002175316],"category_scores_gemma":[0.0008744553,0.0001581816,0.00006019506,0.0008856524,0.0001043637,0.0004334946,0.0001035534,0.001175267,0.0002076884],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00041944,"about_ca_system_score_gemma":0.00232938,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009429727,"about_ca_topic_score_gemma":0.00242055,"domain_scores_codex":[0.9978394,0.0001088786,0.0007974871,0.0004469838,0.0003484454,0.0004588148],"domain_scores_gemma":[0.9989538,0.0003001991,0.00007178391,0.0002771825,0.0002092601,0.0001877882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005801811,0.0002190064,0.008493292,0.00319131,0.00003347246,0.0002329585,0.02909882,0.0002979288,0.0006018499,0.01097915,0.0009294427,0.9453426],"study_design_scores_gemma":[0.0002109647,0.001734266,0.009639842,0.03569756,0.0002375798,0.0007334701,0.07702578,0.4464402,0.1312221,0.268851,0.02612035,0.002086893],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8761867,0.002562954,0.001299555,0.1147075,0.001419001,0.001939729,0.000007733855,0.0001637557,0.001713051],"genre_scores_gemma":[0.9941713,0.0001631147,0.001642821,0.003075183,0.0006128032,0.00009007072,0.00002799588,0.00003470351,0.0001820372],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9432557,"threshold_uncertainty_score":0.9971666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5427085924048283,"score_gpt":0.6096807545039306,"score_spread":0.06697216209910228,"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."}}