{"id":"W4285364397","doi":"10.2196/33703","title":"Evaluation of the Clinical, Technical, and Financial Aspects of Cost-Effectiveness Analysis of Artificial Intelligence in Medicine: Scoping Review and Framework of Analysis","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scope (computer science); Health technology; Economic evaluation; Cost-effectiveness analysis; Payment; Medicine; Artificial intelligence; Risk analysis (engineering); Health care; Management science; Cost effectiveness; Computer science; Engineering; Pathology; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01135711,0.00009608805,0.00119221,0.0005290438,0.00005067239,0.000001719624,0.0001636473,0.0001533586,0.0003276297],"category_scores_gemma":[0.01206925,0.00006907582,0.0001745794,0.004342018,0.0007233043,0.00005502997,0.0001332072,0.0004536797,1.354764e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007183613,"about_ca_system_score_gemma":0.001047141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002146694,"about_ca_topic_score_gemma":0.0002670526,"domain_scores_codex":[0.9950489,0.0006102877,0.002622522,0.0001247464,0.001469709,0.0001237957],"domain_scores_gemma":[0.9959096,0.001885515,0.001074713,0.0003681848,0.0006618385,0.0001001393],"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":[0.0003539491,0.001482534,0.1641491,0.02789051,0.001637057,0.000001439847,0.01514234,0.002254916,0.00009835949,0.007818454,0.00007182539,0.7790995],"study_design_scores_gemma":[0.0005554028,0.003618002,0.521332,0.07804386,0.05967501,0.00002125021,0.02372305,0.2855467,0.004661968,0.02224877,0.00004918965,0.0005247383],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9862979,0.004076888,0.006213684,0.0007647751,0.0001337571,0.002355857,0.00001381874,0.000005173351,0.0001381243],"genre_scores_gemma":[0.9960522,0.003059707,0.000402205,0.00031171,0.00002382882,0.0001317029,0.00001482633,0.000003478845,3.228432e-7],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7785748,"threshold_uncertainty_score":0.9962525,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2385034995205139,"score_gpt":0.5470788065706624,"score_spread":0.3085753070501486,"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."}}