{"id":"W4411325510","doi":"10.1007/s10916-025-02218-8","title":"Artificial Intelligence for Chronic Disease Screening in Latin America and the Caribbean: The Diagnostic Potential of Digital Health","year":2025,"lang":"en","type":"letter","venue":"Journal of Medical Systems","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Latin Americans; Health informatics; Digital health; Disease; Caribbean region; Medicine; Artificial intelligence; Computer science; Public health; Pathology; Political science; Health care","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":[0.004611428,0.0003514508,0.0007031373,0.0007335946,0.003449515,0.003800015,0.0008680315,0.02585205,0.007226077],"category_scores_gemma":[0.02727426,0.0003138554,0.0007042902,0.0007363317,0.00361286,0.004326734,0.002089251,0.02137735,0.001565165],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003813447,"about_ca_system_score_gemma":0.008115127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04537915,"about_ca_topic_score_gemma":0.07480531,"domain_scores_codex":[0.9971521,0.001461856,0.0003549246,0.0001597635,0.0005363354,0.0003349343],"domain_scores_gemma":[0.9699501,0.02298758,0.0009921334,0.0004612102,0.003188806,0.00242019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001016772,0.0000811983,0.009674545,0.0002355006,0.00003658906,0.004888809,0.002112108,0.00009731593,0.0004232261,0.01641015,0.9100147,0.05592409],"study_design_scores_gemma":[0.0001532597,0.00007756054,0.006556424,0.002056201,0.00007236038,0.003813731,0.007342038,0.0009657819,0.0002981084,0.02643219,0.9521487,0.00008373147],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.0005511819,0.001163223,0.00003414014,0.9944655,0.001630999,0.000004112011,0.00001486736,0.000003065539,0.002132869],"genre_scores_gemma":[0.0157022,0.003781138,0.0003283402,0.9626032,0.01218117,0.0000311134,0.00002006416,0.00001325275,0.005339633],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.04537915,"threshold_uncertainty_score":0.09023005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09531778436371575,"score_gpt":0.3998380710048681,"score_spread":0.3045202866411524,"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."}}