Sources of bias in artificial intelligence that perpetuate healthcare disparities—A global review
Beth Israel Deaconess Medical Center · Harvard University · +7 more institutions
Abstract
Background While artificial intelligence (AI) offers possibilities of advanced clinical prediction and decision-making in healthcare, models trained on relatively homogeneous datasets, and populations poorly-representative of underlying diversity, limits generalisability and risks biased AI-based decisions. Here, we describe the landscape of AI in clinical medicine to delineate population and data-source disparities. Methods We performed a scoping review of clinical papers published in PubMed in 2019 using AI techniques. We assessed differences in dataset country source, clinical specialty, and author nationality, sex, and expertise. A manually tagged subsample of PubMed articles was used to train a model,…
Citation impact
- FWCI
- 23.68
- Percentile
- 100%
- References
- 55
Authors
15- LALeo Anthony Celi
Beth Israel Deaconess Medical Center, Harvard University, Massachusetts Institute of Technology
- JCJacqueline Cellini
Harvard University
- MCMarie‐Laure Charpignon
Massachusetts Institute of Technology
- ECEdward Christopher Dee
Harvard University
- FDFranck Dernoncourt
Adobe Systems (United States)
Topics & keywords
- Specialty
- Artificial intelligence
- Population
- Homogeneous
- MEDLINE
- Health care
- Nationality
- Big data