{"id":"W4390739345","doi":"10.21203/rs.3.rs-3833999/v1","title":"Artificial Intelligence in Medical Education- Perception Among Medical Students","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Transformative learning; Medical education; Health care; Curriculum; Inclusion (mineral); Likert scale; Perception; Psychology; Scale (ratio); Nursing; Medicine; Pedagogy; Social psychology","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":[0.003148926,0.0002045746,0.0002783168,0.001326392,0.001028601,0.004329685,0.0004307032,0.001186372,0.006949582],"category_scores_gemma":[0.0157048,0.00022774,0.0003337042,0.0008610791,0.001093891,0.001570711,0.001725644,0.002233763,0.0008358124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009412945,"about_ca_system_score_gemma":0.001387988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007471421,"about_ca_topic_score_gemma":0.005247985,"domain_scores_codex":[0.9979697,0.0009182663,0.0001057977,0.0001759634,0.0004588392,0.0003714273],"domain_scores_gemma":[0.9865194,0.005457034,0.002389501,0.0003146756,0.001860375,0.00345909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000228905,0.001289562,0.9475613,0.00006351901,0.00004163192,0.0001895179,0.02790464,0.0001672791,0.0009962079,0.001482528,0.0008399183,0.01923495],"study_design_scores_gemma":[0.000034507,0.0005319381,0.9029123,0.0001184474,0.0000558246,0.0003926746,0.08526272,0.001577536,0.0009258075,0.002197073,0.00594788,0.00004328792],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9940859,0.0002324469,0.000121771,0.001185636,0.0000281504,0.00001283142,0.00003159732,0.00000303296,0.004298636],"genre_scores_gemma":[0.9980769,0.000152428,0.00006236805,0.0002225856,0.00001289856,0.000004253932,0.00002218307,0.000002769782,0.001443598],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007471421,"threshold_uncertainty_score":0.02324861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.255968849194553,"score_gpt":0.6004188636942119,"score_spread":0.3444500144996589,"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."}}