Lack of diverse datasets in AI research puts patients at risk, experts suggest

New research published in PLOS Digital Health is calling attention to disparities in artificial intelligence that could inhibit its ability to be effectively deployed in clinical settings. Researchers analyzed more than 30,000 artificial intelligence clinical papers published in PubMed in 2019 and found that more than 50% of AI studies utilized databases from the U.S. or China, and that almost all the top 10 databases and author nationalities were from high income countries. Such homogenous datasets, the authors explained, can create research bias that hinders the clinical efficacy of AI applications. "The introduction of AI into healthcare comes with its own biases and disparities; it risks thrusting the world toward an exaggerated state of healthcare inequity," William Greig Mitchell, of the Harvard TH Chan School of Public Health in Boston, Massachusetts, and co-authors wrote. "Repeatedly feeding models with relatively homogeneous data, suffering from a lack of diversity in terms of underlying patient populations and often curated from restricted clinical settings, can severely limit the generalizability of results and yield biased AI-based decisions."