{"id":"W3194775808","doi":"10.4018/ijhisi.20211001.oa17","title":"Artificial Intelligence for Healthcare in India","year":2021,"lang":"en","type":"article","venue":"International Journal of Healthcare Information Systems and Informatics","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Health care; Framing (construction); Government (linguistics); Healthcare policy; Healthcare system; Health sector; Healthcare industry; Business; Computer science; Knowledge management; Health policy; Medicine; Economic growth; Engineering; Health services; Health care reform; Economics","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":[0.002766839,0.0002064938,0.0005476463,0.0007361812,0.0003310084,0.0001363398,0.0003852278,0.0003592067,0.00002981453],"category_scores_gemma":[0.001545096,0.0001904085,0.0001140774,0.0003754339,0.00006195761,0.002348771,0.0001198738,0.0009277519,0.00005931766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006535444,"about_ca_system_score_gemma":0.001988437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008356659,"about_ca_topic_score_gemma":0.0005168261,"domain_scores_codex":[0.9925951,0.00031522,0.005633524,0.00009679036,0.0008523256,0.000507019],"domain_scores_gemma":[0.989607,0.0009308809,0.002523113,0.0002095574,0.006440272,0.0002891179],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.0006394187,0.00009203659,0.07514008,0.00494452,0.0001233247,0.00004006045,0.08130855,0.001184719,0.00001349522,0.6252148,0.001792549,0.2095065],"study_design_scores_gemma":[0.001201985,0.001001171,0.01251384,0.007234605,0.00003640717,0.0008783737,0.566678,0.05916639,0.0006290223,0.02457774,0.3251697,0.0009127621],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5998059,0.00858039,0.1915725,0.1135677,0.06524263,0.0095132,0.001961835,0.0002529466,0.009502872],"genre_scores_gemma":[0.9904776,0.0009514029,0.003179552,0.004336993,0.0008150711,0.00008897554,0.0001071167,0.00001369446,0.00002957081],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.600637,"threshold_uncertainty_score":0.7764634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1243282323868434,"score_gpt":0.4637502677376325,"score_spread":0.3394220353507891,"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."}}