{"id":"W4293150225","doi":"10.1002/smll.202203169","title":"Recent Advances of Utilizing Artificial Intelligence in Lab on a Chip for Diagnosis and Treatment","year":2022,"lang":"en","type":"review","venue":"Small","topic":"Microfluidic and Bio-sensing Technologies","field":"Engineering","cited_by":86,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Microfluidics; Computer science; Personalized medicine; Nanotechnology; Lab-on-a-chip; Throughput; Biochemical engineering; Artificial intelligence; Engineering; Biology; Bioinformatics; Materials science","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":[0.0007029125,0.0009147091,0.0009008045,0.002096973,0.0002506856,0.001024576,0.0009211633,0.001119251,0.003489117],"category_scores_gemma":[0.0007761387,0.0003650573,0.0006380107,0.002080288,0.0005217166,0.001475403,0.0008442237,0.001721366,0.001957092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004710127,"about_ca_system_score_gemma":0.000681627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006698438,"about_ca_topic_score_gemma":0.0007709214,"domain_scores_codex":[0.9996485,0.00006172036,0.00003383791,0.00006381163,0.0001593444,0.00003283908],"domain_scores_gemma":[0.9996165,0.0002114154,0.00003535962,0.00001747957,0.00009378765,0.000025345],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004391289,0.00008635065,0.0001803442,0.01277031,0.00009080385,0.0001810548,0.00008028864,0.0009643562,0.005620032,0.01360804,0.02284065,0.9435339],"study_design_scores_gemma":[0.000009119098,0.0001116529,0.0004683651,0.00150017,0.00009082546,0.0005857481,0.00004305723,0.0006585376,0.002238968,0.004710321,0.9895514,0.00003195064],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0003687237,0.9919308,0.002090178,0.0005096974,0.0004134834,0.00001430702,0.0000276855,0.00003641773,0.004608801],"genre_scores_gemma":[0.002128791,0.9944343,0.001361979,0.0003536907,0.0003264936,0.00001972529,0.00004175107,0.00000516842,0.00132824],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003489117,"threshold_uncertainty_score":0.0116722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1684443060001589,"score_gpt":0.3256079395352137,"score_spread":0.1571636335350548,"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."}}