{"id":"W4392672760","doi":"10.1016/j.ebiom.2024.105047","title":"Drop the shortcuts: image augmentation improves fairness and decreases AI detection of race and other demographics from medical images","year":2024,"lang":"en","type":"article","venue":"EBioMedicine","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Minority Health and Health Disparities; National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Fogarty International Center; Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Gordon and Betty Moore Foundation; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Radiological Society of North America; BioClinica; Pfizer; Biogen; National Heart, Lung, and Blood Institute; Novartis Pharmaceuticals Corporation; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; Alzheimer's Association; National Science Foundation; University of Southern California; National Science and Technology Council","keywords":"Demographics; Medicine; Magnetic resonance imaging; Artificial intelligence; Computer science; Machine learning; Demography; Radiology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003448161,0.001234499,0.0009157342,0.0005608209,0.0004540163,0.001260909,0.001255761,0.001371909,0.002261116],"category_scores_gemma":[0.0123874,0.000344454,0.001074543,0.0002704601,0.0008167579,0.001705067,0.001687597,0.001882985,0.0005923157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007270658,"about_ca_system_score_gemma":0.0009545002,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004464549,"about_ca_topic_score_gemma":0.003806732,"domain_scores_codex":[0.9987967,0.0004171924,0.00006677485,0.0003792709,0.0002085138,0.0001315074],"domain_scores_gemma":[0.9961615,0.002103116,0.0004191496,0.0006910227,0.0004010328,0.0002242011],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003548061,0.001460403,0.09631843,0.0004416477,0.0005464961,0.00047164,0.0007202832,0.2013984,0.03149715,0.002658109,0.01052312,0.6504163],"study_design_scores_gemma":[0.00008817106,0.0009290566,0.01344878,0.00009490566,0.0001337282,0.000357752,0.0001357396,0.9656162,0.01333883,0.003836046,0.001980194,0.00004055762],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7875735,0.002827984,0.1978281,0.001851994,0.0005439029,0.00028189,0.0006447464,0.004026865,0.004420971],"genre_scores_gemma":[0.9605647,0.0002457207,0.03592936,0.0006845621,0.0001150931,0.00006858196,0.0007582841,0.0001119701,0.001521684],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004464549,"threshold_uncertainty_score":0.0182358,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03583418101470353,"score_gpt":0.3995181496081336,"score_spread":0.3636839685934301,"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."}}